Tag Archives: Surgical Technology

Cumulative incidence of retained common bile duct stones on postoperative endoscopic retrograde cholangiopancreatography Active intervention versus surveillance per common bile duct (CBD) stone size. IOC, intraoperative cholangiography; ECRP, endoscopic retrograde cholangiopancreatography.

Video: Intervention versus surveillance in patients with common bile duct stones detected by intraoperative cholangiography

Each year 13 000 patients undergo cholecystectomy in Sweden, and routine intraoperative cholangiography (IOC) is recommended to minimize bile duct injuries. IOC plus an intervention to remove CBD stones identified during cholecystectomy was associated with reduced risk for retained stones and unplanned ERCP, even for the smallest asymptomatic CBD stones in this BJS study.

Increased level of fluorescence intensity of breast cancer and normal mammary gland tissue a Haematoxylin and eosin-stained sections of normal and cancer tissue. The area outlined in green indicates the breast cancer. b Top images are fluorescence intensity images of γ-glutamyl hydroxymethyl rhodamine green (gGlu-HMRG) (green) at each time point overlaid on white light images. The fluorescence increase (FI) was obtained by subtracting the baseline fluorescence from the fluorescence at each time point. FIs are represented by the pseudocolour scale on the right side of the image.

Guest blog: a novel fluorescence technique for detecting breast cancer

Author: Hiroki Ueo, Department of Surgery and Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Ueo Breast Cancer Hospital, Oita, Japan

Breast cancer is the most common cancer in women, and its incidence continues to increase worldwide. From the patient’s perspective, breast conserving surgery (BCS) with radiation achieves a balance between a satisfactory cosmetic result and a low recurrence rate. Although it has been established as a routine surgery, surgeons need to be careful about positive surgical margins. Remnant cancer cells in the preserved tissue increase the risk of recurrence. Therefore, a positive margin on postoperative pathology warrants additional surgery. In these cases, the additional treatment harbours unexpected outcomes, including physical, mental, cosmetic, and economic burden on the patients. 

To avoid the additional operation, pathological evaluation using an intraoperative frozen section is conducted. It is the most reliable method to prevent misdiagnosis and to achieve clear surgical margins. However, this conventional method is time consuming and costly. Moreover, it is dependent on the skill and experience of the pathologists and personnel, and it requires space for preparation of the frozen sections. Therefore, only a limited number of samples are examined to save time and resources. An alternative, rapid, and reliable technique to detect cancer in surgical margins enables simultaneous testing, leading to a reduced false negative rate of local recurrence incidence. In addition, pathologists can focus on the definitive diagnosis using permanent paraffin sections because it is difficult to make a diagnosis based on intraoperative frozen sections without pathological architecture. Pathologists only need to make an intraoperative diagnosis when the specimen cannot be evaluated via the fluorescence procedure. Thus, it is important to enhance the rapid fluorescent detection of breast cancer during surgery. To address these diagnostic issues, Prof. Urano invented chemical reagents (gamma-glutamyl hydroxymethyl rhodamine green [gGlu-HMRG]) that quickly fluoresce by reacting with an enzyme (gamma-glutamyl transferase [GGT]), overexpressed in cancerous tissues. It exhibits strong fluorescence a few minutes after reacting with GGT in vitro. A gGlu-HMRG solution is applied to the surgical margins to recognize cancer cells as green fluorescence intraoperatively. A previous study in 2015 documented the ability of this reagent to mark cancerous tissues in surgical breast tissues. Furthermore, this reagent did not interfere with the pathological examination, while the frozen section analysis tissues were difficult to reuse as formalin-fixed and paraffin-embedded permanent pathological specimens. 

The clinical utility of this technique was examined. The results were published in the British Journal of Surgery. Since the initial report in 2015, a more feasible and reproducible sample preparation protocol has been developed. Then, a dedicated apparatus, including a built-in camera, software program, and multiple sample wells, was developed. This system automatically measured and analyzed the increase in fluorescence of multiple samples simultaneously. Then, the increase in fluorescence of gGlu-HMRG, applied to breast tissues, was measured in four different institutes. The sample tissues were examined by four pathologists independently. These pathologists diagnosed the samples without knowing the background information of the patients. The clinical utility of the current fluorescent procedure was evaluated by comparing the fluorescence data and the pathological diagnosis. 

A clear threshold to distinguish between cancerous and non-cancerous tissues was not determined due to the heterogeneity of breast cancer tissues. Instead, the negative threshold to achieve a false negative rate <2% and the positive threshold to achieve a false positive rate <2% were established. Samples in which the increase in fluorescence was below the negative threshold value were considered cancer-free margins with a false negative rate <2%. The false negative samples in our study were tissues containing non-invasive cancer. This suggested that the samples below the negative threshold can be considered free of invasive cancer. Samples in which the increase in fluorescence was above the positive threshold value were considered cancerous tissue with a false positive rate <2%.

The disease prevalence determines the performance of a diagnostic tool. The percentage of positive and negative test results among those with or without the disease are the positive and negative predictive values, respectively. These positive and negative predictive values depended on the prevalence. Therefore, to estimate the performance of this technique, the prevalence and margin positive rate in this case should be considered. The margin positive rate was expectedly lower than that of our clinical study. In our protocol, three pieces of tissue were sampled: the central portion, where the breast cancer is located; its periphery, which contains non-invasive cancer; and the distal portion, which ideally contains normal mammary tissue. Cancer was detected in 46% of the samples. Based on the actual margin assessment, the prevalence was lower than that of our study. Assuming a prevalence <30%, the negative predictive value, the ratio of true negative samples among fluorescent negative samples, was larger than 98%. This indicated that this method was useful for detecting negative margins. 

According to this multicenter study, the fluorescent diagnosis was applicable to any breast cancer subtype, regardless of its pathological findings and subtype. Moreover, the similar accuracy among several institutes confirmed that the fluorescent diagnosis was applicable to any institute, following the protocol. Compared to the intraoperative frozen section analysis, the fluorescent diagnosis was a more rapid and accessible method with a low cost. It was not dependent on the skills of pathologists, and it did not require a large amount of space.           

In conclusion, this method can facilitate the rapid assessment of negative surgical margins during BCS while reducing the testing time, cost of diagnosis, and tasks of the pathologists and staff. 

External aspect of the operative field: DaVinci™ robotic system docked to the patient

Guest blog: What advantage does robot-assisted and transanal TME have over laparoscopy?

Authors: Jeroen C. Hol, Colin Sietses

Contact: j.c.hol@amsterdamumc.nl

Correspondence to: “Comparison of laparoscopic versus robot-assisted versus TaTME surgery for rectal cancer: a retrospective propensity score matched cohort study of short-term outcomes

Image source: Robinson Poffo et. al. Robotic surgery in Cardiology: a safe and effective procedure. https://creativecommons.org/licenses/by/4.0/ under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The emergence of minimally invasive surgery has led to the development of three new surgical techniques for oncological rectal resections: laparoscopic, robot-assisted and transanal TME (TaTME). When we compared the three techniques executed in expert centres, we expected to find an advantage for one of the three techniques in terms of reduced complication rates. But contrary to our expectations, no difference was seen. There was one striking difference however, when comparing these techniques, though it might be something different than you might have thought. We shine a light on all three techniques to explain their advantages. 

Laparoscopy: minimally invasive surgery

In the 1980’s, Heald introduced the total mesorectal excision (TME) principle, which comprises excision of the rectum and its surrounding fatty envelop with preservation of the autonomic nerves [1]. TME has become the golden standard for surgical resection for rectal cancer and helped dropping local recurrence rates drastically. The past decades laparoscopy has been introduced and gradually replaced open surgery. Laparoscopy offers short term benefits of minimally invasive surgery, such as faster recovery and reduced complication rates [2, 3]. It offers similar long-term outcome as open surgery [4]. But laparoscopy is technically demanding because it is difficult to work with rigid instruments in the narrow and confined area of the pelvis. Therefore, conversion rates to open surgery of more than 10% were seen [5]. Conversion is linked to increased morbidity and worse oncological outcome [6]. In order to overcome those technical limitations of laparoscopic TME, new techniques have been introduced; robot-assisted TME and TaTME. 

Robot-assisted TME: the same, but different

Robot-assisted TME comprises the same approach as laparoscopy, but with the use of a surgical robot. The surgical robot provides a stable platform with supreme vision and supreme instrument handling. Surgeons thought this technique might improve results in terms of reduced complication rates and reduced conversion rates. However, the largest randomized trial so far comparing robot-assisted and laparoscopic TME failed to show any difference in these outcomes [7]. This might have been the result of a methodological flaw, because the robotic surgeons in that trial were not as experienced as their laparoscopic colleagues [8]. In our study, we tried to eliminate this by only selecting experienced centres that were beyond their learning curve. However, we did not see reduced complication rates or reduced conversion rates after robot-assisted TME compared to laparoscopy.

Transanal TME: a different approach

TaTME comprises a different approach to address the most difficult part of the dissection. In TaTME the most distal and difficult part of the rectum is dissected from below using a transanal insufflator port. However, this is a technically demanding technique and has a long learning curve [9]. Some initial series showed high loco regional recurrence rates, which even led to a halt of TaTME in Norway [10, 11]. The potential learning curve effect is now part of an ongoing debate about the oncological safety of this technique. Most initial results however looked promising and showed consistently good quality specimen and lower conversion rates [12, 13]. In our study, conversion rates, number of complete specimen and morbidity rates did not differ from the other laparoscopy and robot-assisted TME. 

Technological advantage 

The results of our study showed similar and acceptable short-term results for all three techniques in expert centres. The most striking difference was that in centres with robot-assisted or TaTME, more primary anastomoses were made. The technological advantage of the two new techniques could have contributed to higher restorative rates. Both robot-assisted and TaTME provide better access and visibility to the distal rectum, enabling surgeons to complete the TME dissection safely and create an anastomosis. Robot-assisted TME could overcome technical limitations of laparoscopy in the narrow pelvis thanks to the use of 3D vision, lack of tremor, and superior instrument handling, thereby facilitating safe creation of an anastomosis [7, 14]. TaTME does not need multiple staple firing to transect the distal rectum and without requiring conversion to open surgery [13]. In fact, TaTME does not need cross-stapling at all, preventing the creation of dog-ears which are prone to ischemia [15]. 

Patient’s perspective

In conclusion, the technological advantage of robot-assisted TME and TaTME manifests itself in higher restorative rates. Each technique seems to be equally beneficial in terms of oncological outcomes and morbidity. However, anastomosis creation, quality of life and functional outcome are becoming of great importance to patients. It seems to be that an increasing proportion of patients is now in pursue of an anastomosis. The overall anastomosis rate of more than 84% for robot-assisted and TaTME in our study was higher than the anastomosis rate of 50% in a previous national study [16]. A note of caution should be added, as an anastomosis might not be always better in terms of functional outcome and quality of life. Patients with a low anastomosis are at risk of developing severe low anterior resection syndrome (LARS) symptoms. Severe LARS symptoms can have a detrimental effect on quality of life [17].  Further research should be undertaken to investigate whether a higher anastomosis rate is beneficial in terms of quality of life and functional outcome and whether this higher anastomosis rate actually leads to increased patient satisfaction. 


1.         Heald, R.J., E.M. Husband, and R.D. Ryall, The mesorectum in rectal cancer surgery–the clue to pelvic recurrence? Br J Surg, 1982. 69(10): p. 613-6.

2.         Stevenson, A.R., et al., Effect of Laparoscopic-Assisted Resection vs Open Resection on Pathological Outcomes in Rectal Cancer: The ALaCaRT Randomized Clinical Trial. JAMA, 2015. 314(13): p. 1356-63.

3.         van der Pas, M.H., et al., Laparoscopic versus open surgery for rectal cancer (COLOR II): short-term outcomes of a randomised, phase 3 trial. Lancet Oncol, 2013. 14(3): p. 210-8.

4.         Bonjer, H.J., et al., A Randomized Trial of Laparoscopic versus Open Surgery for Rectal Cancer. N Engl J Med, 2015. 373(2): p. 194.

5.         Chen, K., et al., Laparoscopic versus open surgery for rectal cancer: A meta-analysis of classic randomized controlled trials and high-quality Nonrandomized Studies in the last 5 years. Int J Surg, 2017. 39: p. 1-10.

6.         Allaix, M.E., et al., Conversion of laparoscopic colorectal resection for cancer: What is the impact on short-term outcomes and survival? World J Gastroenterol, 2016. 22(37): p. 8304-8313.

7.         Jayne, D., et al., Effect of Robotic-Assisted vs Conventional Laparoscopic Surgery on Risk of Conversion to Open Laparotomy Among Patients Undergoing Resection for Rectal Cancer: The ROLARR Randomized Clinical Trial. JAMA, 2017. 318(16): p. 1569-1580.

8.         Corrigan, N., et al., Exploring and adjusting for potential learning effects in ROLARR: a randomised controlled trial comparing robotic-assisted vs. standard laparoscopic surgery for rectal cancer resection. Trials, 2018. 19(1): p. 339.

9.         Koedam, T.W.A., et al., Transanal total mesorectal excision for rectal cancer: evaluation of the learning curve.Tech Coloproctol, 2018. 22(4): p. 279-287.

10.       Larsen, S.G., et al., Norwegian moratorium on transanal total mesorectal excision. Br J Surg, 2019. 106(9): p. 1120-1121.

11.       van Oostendorp, S.E., et al., Locoregional recurrences after transanal total mesorectal excision of rectal cancer during implementation. Br J Surg, 2020.

12.       Detering, R., et al., Three-Year Nationwide Experience with Transanal Total Mesorectal Excision for Rectal Cancer in the Netherlands: A Propensity Score-Matched Comparison with Conventional Laparoscopic Total Mesorectal Excision. J Am Coll Surg, 2019. 228(3): p. 235-244 e1.

13.       Grass, J.K., et al., Systematic review analysis of robotic and transanal approaches in TME surgery- A systematic review of the current literature in regard to challenges in rectal cancer surgery. Eur J Surg Oncol, 2019. 45(4): p. 498-509.

14.       Kim, M.J., et al., Robot-assisted Versus Laparoscopic Surgery for Rectal Cancer: A Phase II Open Label Prospective Randomized Controlled Trial. Ann Surg, 2018. 267(2): p. 243-251.

15.       Penna, M., et al., Four anastomotic techniques following transanal total mesorectal excision (TaTME). Tech Coloproctol, 2016. 20(3): p. 185-91.

16.       Borstlap, W.A.A., et al., Anastomotic Leakage and Chronic Presacral Sinus Formation After Low Anterior Resection: Results From a Large Cross-sectional Study. Ann Surg, 2017. 266(5): p. 870-877.

17.       Emmertsen, K.J. and S. Laurberg, Low anterior resection syndrome score: development and validation of a symptom-based scoring system for bowel dysfunction after low anterior resection for rectal cancer. Ann Surg, 2012. 255(5): p. 922-8.

Schematic of process for classifier design

Guest blog: 21st century surgery is digital

Ronan Cahill, Digital Surgery Unit, Mater Misericordiae University Hospital, Dublin, Ireland and UCD Centre for Precision Surgery, Dublin, Ireland.

Niall Hardy, UCD Centre for Precision Surgery, Dublin, Ireland.

Pol MacAonghusa, IBM Research, Dublin, Ireland.

Twitter @matersurgery Email: ronan.cahill@ucd.ie

Cancerous tissue behaves differently from non-cancerous tissue. Every academic oncology paper ever written tells us this. The appearances of any cancer primary (or indeed secondary lesion) result from biological and molecular processes that are the hallmarks of malignancy including dysregulated cell function and composition, host-cancer stromal and inflammatory response and angiogenesis. However, we surgeons haven’t really yet been able to exploit this knowledge during surgery in a way that helps us make a better operation. Instead, our learning and research about oncological cellular processes has predominantly advanced through basic science geared more towards perioperative prognostication and/or adjuvant therapy stratification. Wouldn’t it be great if realisation of cancer microprocesses could usefully inform decision-making intraoperatively?

We’ve just published an initial report in the BJS showing this very thing – that it is indeed possible to ‘see’ cancer by its behaviour in real-time intraoperatively. We’ve used Artificial Intelligence (AI) methods in combination with near-infrared fluorescence laparoendoscopy to judge and classify neoplastic tissue nature through the observation of differential dye diffusion through the region of interest in comparison with that happening in normal tissue being viewed alongside it. Through our understanding of biophysics (flow parameters and light/dye interaction properties), a lot of information can be drawn out over short periods of times via advanced computer vision methodology. With surgical video recording in the region of 30 frames per second, big data generates over the time frame of a few minutes.  While the gross signal shifts are discernible even without AI, smart machine learning capabilities certainly mean their interrogation becomes really usable in the provision of classification data within moments. What’s more, while we’ve focused initially on colorectal cancer, the processes we are exploiting seem common across other solid cancers and using other camera-based imaging systems. By combining with the considerable amount of knowledge we already have accrued regarding tissue biology, chemistry, physics as relate and indeed surgery, our AI methods are giving explainable and more importantly interpretable recommendations with confidence using a smaller dataset than that demanded by deep learning methodologies.

This though is just an early exemplar of what’s becoming possible through ‘Digital Surgery’, a concept that seems far more likely to transform contemporary surgical practice than our current general surgery “robotic” systems, hulking electromechanical tools entirely dependent on the user – a rather 20th century concept! Indeed, there is sophisticated technology everywhere in today’s operating theatres – surgeons sure don’t lack technical capability. Yet often despite vaulting costs, advance of real, value-based outcomes has been disappointingly marginal in comparison over the last two decades. The key bit for evolved surgery is instead going to be assisting surgeons to make the best decision possible for each individual patient by providing useful, discerning information regarding the surgery happening right now, and somehow plugging this case circumstances directly into the broad knowledge bank of expertise we have accrued as a profession (and not just be dependent on any single surgeon’s own experience).

To do this we need to realise the importance of visualisation in surgical procedures versus manual dexterity.  All surgery is performed through the visual interpretation of tissue appearances and proceeds via the perception-action cycle (‘sense, predict, act, adjust’). This is most evident during minimally invasive operations where a camera is used to display internal images on a screen but applies of course to open procedures as well. As all intraoperative decisions are made by the surgeon, the entire purpose of surgical imaging has been to present the best (‘most visually appealing’) picture to the surgeon for this purpose. Experiential surgical training is for the purpose of developing the ‘surgical eye’, that is learning how to make qualitative intraoperative judgments reliably to a reasonable standard. We haven’t however gotten the most out of the computer attached to the camera beyond image processing where we have concerned ourselves with display resolutions and widths. 

Imagine instead if some useful added interpretations of images could be made without adding extra cognitive burden to the surgeon, perhaps with straightforward on-screen prompts to better personalise decisions? This would be particularly exciting if these data were not otherwise easily realisable by human cognition alone and could be immediately and directly relevant to the person undergoing the operation. Every operation is in effect a unique undertaking, informed by probabilities accruing through individual and collected prior experience for sure but a new thing in and of itself for which the outcome at the time of its performance is unknown. How this individual patient differs from others and most especially how might an adverse outcome be avoided is a crucial thing to flag before any irreversible surgical step that commits an inevitable future. 

Right now, we are in a golden age of imaging. This is intricately linked to advances in computer processing and sharing power along with AI methods. This means we can harvest great additional information from the natural world around us across the spectrum of enormous (radio waves spanning the universe) to tiny (high resolution atomic imaging) distances and apply methods to help crystalise what this means to the observer. While a lot of AI is being directed at the easier and safer areas of standard patient cohort datasets, increasingly it’s possible to apply computer intelligence to the data rich surgical video feeds being generated routinely during operations to present insights to the surgeon. While early first steps at the moment relate to rather bread and butter applications such as instrument or lesion recognition and tracking as well as digital subtraction of smoke or anonymization protocols to prevent inadvertent capture of operating rooms teams when the camera is outside the patient, soon the capability to parse, segment and foretell likely best next operative steps will be possible at scale.

At present, the biggest limitation is that surgery lacks large warehoused archives of annotated imagery because operative video is a more complex dataset to scrutinise than the narrower image datasets available in specialities such as radiology, pathology and ophthalmology. Thanks to advances in computing, this is changing. Surgical video aggregation to enable building of representative cohorts is increasingly possible and, by combining with metadata and surgical insights, its full value can begin to be realised. GDPR frameworks provide structure and surgeons are increasingly understanding of the value of collaborating in research, education and practice development. However, while certain siloed sites focused around specific industry projects are already manifesting, the key area for greatest general advance lies within the surgical community combining broadly to construct appropriately developed and secured, curated video banks of procedures that can then be made accessible to entities from regulators and standard bodies, academia and indeed corporations capable of advancing surgery. This gives by far the greatest chance of the best of surgical traditions carrying through the 21st century while our weak spots are fortified for better surgery in the public interest.

Further reading: 
Artificial intelligence indocyanine green (ICG) perfusion for colorectal cancer intra-operative tissue classification.
 Cahill RA, O’Shea DF, Khan MF, Khokhar HA, Epperlein JP, Mac Aonghusa PG, Nair R, Zhuk SM.Br J Surg. 2021 Jan 27;108(1):5-9. https://doi.org/10.1093/bjs/znaa004 PMID: 33640921 

The age of surgical operative video big data – My bicycle or our park? Cahill RA, MacAonghusa P, Mortensen N. The Surgeon 2021 Epub ahead of press https://doi.org/10.1016/j.surge.2021.03.006

Ways of seeing – it’s all in the image. Cahill RA. Colorectal Dis. 2018 Jun;20(6):467-468. https://doi.org/10.1111/codi.14265 PMID: 29864253

NodeXL graph of twitter users whose tweets or mentions contained the hashtags #SoMe4Surgery and #SurgicalTechnology. The graph is directed. The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm. The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.

Proceedings of the #SoMe4Surgery tweetchat on the future of surgical technology


1. Rebecca C Grossman MA MBBS AKC DHMSA MRCS, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK 

2. Graham Mackenzie MD FRCPE, Penicuik Medical Practice, Imrie Place, Penicuik, UK

3. Julio Mayol MD PhD, Professor of Surgery, Chief Medical Officer, Hospital Clinico San Carlos, Instituto de Investigación Sanitaria San Carlos, Universidad Complutense, Madrid, Spain. 

Competing interests: The authors declare no competing interests.

Funding: No funding was provided for this study.

Previous presentations: The findings of this study were presented as a poster at the Society for Surgery of the Alimentary Tract 60th Annual Meeting on the 21st May 2019. 


It has long been the tradition to publish the Proceedings of surgical conferences 1. Over the last 15 years, the way surgeons interact has transformed extensively due to the advent of social media, with much of the conversation moving online 2-3. The covid-19 pandemic acted to accelerate this transition 4. The microblogging platform Twitter provides a vast library of information and allows real-time communication and dissemination of information, grouped along themes via a “hashtag” (metadata tag) 5-8. Twitter use is increasing among surgeons, researchers, healthcare professionals, and patients.

Organised conversations on Twitter, so-called “tweetchats”, are a forum through which experts, trainees, and patients from around the globe can communicate and discuss topics of shared interest via a hashtag and moderated by a host 5,7,9. Tweetchats allow real-time back and forth conversation, similar to face-to-face interactions10. These conversations are a treasure-trove of ideas that can provide great insight into the most cutting-edge trends in surgical practice 7,11-13.

On 28th July 2018, a social media initiative was created by Julio Mayol via his Twitter handle (@juliomayol), to focus on specific surgical interests, connected via the hashtag #SoMe4Surgery (Social Media For Surgery) 14. The aim was to bolster a more inclusive, multidisciplinary surgical community. Since the inception of #SoMe4Surgery, a number of tweetchats were planned and undertaken using the hashtag. In November 2018, a tweetchat was held with the subject of surgical technology. This theme was chosen as surgical technology is rapidly evolving in many directions, under multiple influences 15, and the authors felt it was a key time to take stock in where we are and where we are going. The aim of this study was to identify the main themes of the chat on surgical technology and to estimate the potential reach of the tweets.



No ethical approval was required for this retrospective study as it did not interfere with any patient or human data beyond measuring internet activity among Twitter users using publicly available tweets.

Sampling and data extraction

A retrospective analysis was performed of the tweetchat that was led by two surgeons with 941 (@rebgross) and 24,539 (@juliomayol) followers on November 23rd 2018, with ten predefined questions. The #SoMe4Surgery ecosystem was the primary target of the conversation. Ten tweets containing questions for the audience were posted in a 60-minute period (9:00 pm – 10:00 pm Madrid time). 


Data analytics and visualization were carried out using two different online tools. Twitonomy is available at http://www.twitonomy.com, and provides advanced network analytics of tweets, hashtags and tweetchats, under subscription. Twitonomy analytics were performed by author JM on November 29th 2018, of tweets posted between November 22nd 2018 at 8:15 pm and November 29th 2018 at 8:17 pm using the two hashtags, “#some4surgery” and “#surgicaltechnology”. Potential reach was defined as the total aggregate number of followers of the people who mentioned both keywords in their tweets. Potential reach may be overestimated as Twitonomy may make assumptions to estimate impressions and/or audience, and geolcations are sometimes misclassified; therefore NodeXL analytics were also examined by author GM. NodeXL is a spreadsheet template that allows the creation of visual network graphs (Social Media Research Foundation; California, USA; https://www.smrfoundation.org/nodexl/). Using NodeXL, the extracts for 23rd November 2018 were extracted and mapped as described elsewhere 16.

The tweets from the tweetchat were manually reviewed on Twitter by author RG by searching for the terms [#SoMe4Surgery since:2018-11-23 until:2018-11-30] on 18th June 2019 to identify the themes of the chat for content analysis. Replies to the questions posted by the moderators were also reviewed to avoid missing tweets that did not include the hashtag. The handles (usernames) and profiles of the users were manually reviewed.



Twitonomy analytics revealed that, between 22nd November 2018 at 8:15 pm and 29th November 2018 at 8:17 pm, there were 348 tweets and retweets including the two hashtags posted by 60 users (40 men, 13 women, 7 unknown) from 50 geolocations in 5 continents. From the biographical information available in their Twitter profiles, specialties included general surgery (5), HPB/transplant (4), plastics/cosmetic (2), vascular (4), ophthalmology (1), hernia (1), colorectal (5), cardiovascular (1), endocrine/bariatric (1), spinal (1), global (1), trainees (1), associations (1), and non-medical (5). Conflicts of interest of individuals participating in the tweetchat included working for private health tech companies (4), criminal defence lawyer (1), and running the tweetchat (2).

From Twitonomy, the potential reach was 1,883,455 accounts. A tweetmap of the users of both #SoMe4Surgery and #SurgicalTechnology hashtags can be found in Fig. 1. 

Fig. 1. Tweetmap of the users of both #SoMe4Surgery and #SurgicalTechnology hashtags

NodeXL data revealed, over the 1-day, 2-hour, 48-minute period from Thursday, 22nd November 2018 at 19:15 UTC to Friday, 23rd November 2018 at 22:04 UTC, there was a network of 39 Twitter users whose recent tweets contained both #SoMe4Surgery and #surgicaltechnology hashtags (Fig. 2), or who were replied to or mentioned in those tweets. There were 39 vertices, 71 unique edges, 303 edges with duplicates, 374 total edges, and 22 self-loops. Reciprocated vertex pair ratio was 0.19, and reciprocated edge ratio was 0.32. In a connected component, there were 39 maximum vertices and 374 maximum edges.

Fig. 2. NodeXL graph of twitter users whose tweets or mentions contained the hashtags #SoMe4Surgery and #SurgicalTechnology. The graph is directed. The graph’s vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm. The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.

Over the 21-hour, 40-minute period from Friday, 23rd November 2018 at 00:18 UTC to Friday, 23rd November 2018 at 21:59 UTC, there was a network of 152 Twitter users whose recent tweets contained the #SoMe4Surgery hashtag (Fig. 3), or who were replied to or mentioned in those tweets. There were 152 vertices, 329 unique edges, 546 edges with duplicates, 875 total edges, and 44 self-loops. Reciprocated vertex pair ratio was 0.13, and reciprocated edge ratio was 0.22. In a connected component, there were 128 maximum vertices and 848 maximum edges.

Fig. 3. NodeXL graph of twitter users whose tweets or mentions contained the hashtag #SoMe4Surgery. The graph is directed. The graph’s vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm. The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.

From Twitonomy, the ten most influential users (8 men, 1 woman, 1 unknown) had a median number of followers of 16,648 (range 747-3­44,648). The ten most engaged users (4 men, 3 women, 3 unknown) posted a median number of 27 tweets (range 11­-346). The top hashtags were #SoMe4Surgery, #surgicaltechnology, #surgicalpractice, #AI and #SSI.

Current technological improvements to surgical practice

In a poll asking which surgical technology has most significantly improved surgical practice (Question 4), preoperative imaging received the most votes (53% out of 288 votes), with intraoperative imaging receiving 10% of the votes (Fig. 4). @WarrenRozen stated that preoperative imaging is certainly of benefit, while intraoperative imaging has not yet demonstrated this effect despite having great potential. @MrRJEgan stated that preoperative imaging reported by specialists can improve quality and outcomes. @perbinder highlighted that this was particularly important in vascular surgery, where preoperative Duplex and CT angiography are widely used. @SJ_Chapman suggested that in colorectal surgery, medical imaging in general had revolutionised patient care before, during and after surgery, for example with the use of post-processing CT colonography, PET-CT, and MR. @EUrologyReg agreed, stating that the now widespread availability of CT scans has had a huge impact on surgical decision-making.

Fig. 4. Screenshot of Question 4 of the tweetchat. Poll: “Which surgical technology has more significantly improved your surgical practice?”

Energy delivery systems received 33% of the votes. @DrSantiagoOrtiz explained that technologies such as laser, phacoemulsification, and vitrectomy had revolutionised his field of ophthalmology. @MrRJEgan stated that the main benefit of energy devices lies in efficiency and reduced operating times.

Biomaterials received the fewest votes (4%) and were considered more likely to be of benefit in the future (@WarrenRozen), although @DrSantiagoOrtiz thought that they were becoming very relevant in ophthalmology with the use of intraocular lenses.

@A160186 reported that endoscopy and endoluminal surgery have also changed the face of surgery, and that interventional radiology has radically impacted the management of surgical conditions, pointing out that it is a non-surgical technology, and that saving the patient from having an operation should be considered an achievement in itself. @DrSantiagoOrtiz agreed that non-surgical technology will likely have the highest impact in surgical practice.

In a poll asking which surgical technology is most frequently used for intraoperative bleeding (Question 7), 82% of the 102 votes were for energy delivery devices, 10% for fibrin sealants, and 2% for thrombin gels (Fig. 5). @YorkLawLondon stated that intraoperative bleeding can be problematic in fibroid surgery, and that pharmacological therapy, such as preoperative hormone suppressants and intraoperative vasopressin, is frequently used to counter this.

Fig. 5. Screenshot of Question 7 of the tweetchat. Poll: “What surgical technology do you most frequently use for intraoperative bleeding?”

In a poll asking about the use of surgical technology to reduce the rates of surgical site infection in surgical practice (Question 5), 40% voted “yes” (of 78 respondents), 22% voted “sometimes”, and 31% voted “no” (Fig. 6). @DrSantiagoOrtiz expressed surprise by the high proportion answering “no”, stating that the use of such technology is widespread in ophthalmology.

Fig. 6. Screenshot of Question 5 of the tweetchat. Poll: “Do you use any surgical technology to reduce the SSI rates in your surgical practice?”

Future innovations in surgical technology to improve patient safety

A wide range of technological innovations were proposed to improve patient safety in future surgical practice. These included energy devices, advances in anaesthesia, pharmacology, information technology services and data management, radiology and nuclear medicine, and advances in medical allied medical specialities such as gastroenterology, clinical genetics, and medical oncology.

An area in which many Twitter users were interested was navigation-guided surgery, particularly with respect to finding the right planes and avoiding at-risk structures (@dr_samehhany81). @A160186 described a “surgical GPS or an intraoperative Siri/Alexa” to guide surgeons through tough terrain. @polom_karol took this further, adding a preoperative diagnostic tool overlay and the help of artificial intelligence to assist in surgical decision making.

“It would be great if during a lap cholecystectomy [you] could just go ‘Siri [please] tell me if this is the cystic duct’ (hoping she’d have the right answer).” (@A160186)

@Eric_Vibert and @jamestoml1 both highlighted the importance of the OR Black BoxTM in changing the relationship between surgery and human error, which has a significant impact on patient safety. @schnitzb suggested that direct loop feedbacking would lead to a reduction in human error.

The most commonly mentioned technological advance was laparoscopic and robotic surgery. @CelestinoGutirr argued that robotic surgery improves the technical precision of surgery; @alessiominuzzo countered that as it has been introduced as an “instrument” and its indications have altered, its use should be considered “off label surgery”, or should only be in the context of research. It was compared to laparoscopy, with @RNCsantander and @anhanssen suggesting that the outlook for robotics was similar to that of laparoscopy in its early days, and @DrSantiagoOrtiz stating that the evidence has shown laparoscopy to improve patient safety, while the jury is still out for robotics. Overall, the consensus was that, in the future, the evidence would reveal robotic surgery to be beneficial to patient safety.

In a poll asking how robotic surgery will evolve in the future (Question 2), 42% of respondents (134 votes) predicted that robots would be smaller (Fig. 7). Only 11% of respondents thought that robotic surgery would be phased out. @tuttlejebetsy argued that the “the case reimbursement is too low for sustainability and widespread adoption”, suggesting that robotic surgery only has a future as long as it can demonstrate a sustainable, cost-effective return on investment.

Fig. 7. Screenshot of Question 2 of the tweetchat. Poll: “How will robotic surgery evolve in the future?”

@RNCsantander questioned how we can improve the learning curve and training in robotic surgery to generalize its use. The high cost of the technology was felt to be a barrier to its accessibility (@A160186, @RNCsantander, @rcanterocid). @rebgross suggested the use of simulation training, and @A160186 suggested that robotics should be included in training or fellowship programs, arguing that one must have seen it to practise, and subsequently teach, the technique. @JoshuaTylerMD stated that skill monitoring and improved mentorship via online platforms were essential in improving training.

Three-dimensional printing for surgical practice

In a poll, 51% of 164 individuals voted to say that three-dimensional (3D) printing might be useful for surgical practice (Question 6), while 35% said it will have a big impact, and 7% voted for “it’s a fad” (Fig. 8).

Fig. 8. Screenshot of Question 6 of the tweetchat. Poll: “What is your opinion on 3D printing for surgical practice?”

Participants of the tweetchat had found 3D printing to be useful in colorectal (@dr_samehhany81) 17, orthopaedic and maxillofacial (@rcanterocid), and vascular surgery (@TMCAvascular).

@GaneshPuttu and @JasamineCB both stated that 3D printing has been useful in complex cases or with complex anatomy, to assist in visualisation for pre-operative planning, as well as an education tool for trainees and patients, with @MMakgasa suggesting they be used in the consent process. @TMCAvascular called 3D printing fundamental for case planning, posting “before” and “after” images of a ruptured cannulation site pseudo-aneurysm treated with an atrial septal device via brachial approach with intravascular ultrasound and intracardiac echography with the aid of 3D printing.

@LumsdenHMDHVC stated that his centre had moved away from 3D printing for training purposes, for which they used virtual simulation, but that its use was better indicated in case planning and device printing. @jmills1955 thought that the ability of 3D printing to allow the creation of patient and anatomic-specific devices would lead to it having a significant impact. @VerranDeborah echoed its use for implants and extended this to the biofabrication of tissue, suggesting that it may pave the way for the printing of organs in 10-20 years.

Artificial intelligence and its impact on surgical practice

Overall, the consensus was that artificial intelligence (AI) had the potential to have a significant impact on surgical practice. @juliomayol suggested that AI will change the way decisions are made and outcomes are monitored. @dr_samehhany81 and @Dr_A_Sturiale countered that AI would aid and complement the work of humans, but will never replace them.

In particular, it was thought that AI would have the largest impact in medical specialties in which imaging plays a crucial role in diagnosis (@DrSantiagoOrtiz). It was also felt that AI may result in a lower workload and administrative burden, with more time left to devote to direct patient care (@schnitzb). @polom_karol had a dramatic view of AI, stating: “AI will change all”, and that it was “the biggest revolution since [the] early beginning of surgery.” @hgok went so far as to suggest that in only 10 years, appendicectomies and cholecystectomies would be performed by AI-controlled robotic platforms, but that in hernia surgery this would take more time to develop.

@A160186 felt that one of the biggest benefits of AI would be found in patient safety, by creating “safety checkpoints” in clinical decision making, leading to the standardisation of diagnostics and procedures. She added that it was not clear whether AI would ever be autonomous; @YorkLawLondon and @DSoybel suggested this would mean it would only be as good as the data entered, and, for example in diagnostics, a diagnosis could be missed. @DSoybel further posed the critical question of who would control the data and algorithms. @YorkLawLondon added that any outsourcing could lead to companies exploiting or restricting data access and profiting from it; @DSoybel answered: “If neither data nor algorithms are proprietary there would be chaos. If both are proprietary there would be monopoly and potential for gaming. If one is and the other is not, there will be competition.”

“I’m sure surgeons prefer artificial intelligence over lack of intelligence.” @A160186

The patient’s perspective

There were some very insightful answers given when patients were asked which surgical technology they most valued. They can be found here. The most common theme was that the surgeon was valued above the technology.

Ethical issues in the development of new surgical technology

The issue of data ownership again arose when discussing the ethical issues surrounding the development of new surgical technology (@polom_karol).

The evaluation of risks and benefits was frequently mentioned (@CelestinoGutirr, @RNCsantander). Other issues included the dangers of optimism bias (@rebgross), as not all innovations are successful or result in improved patient care, and publication bias (@SJ_Chapman), leading to research waste 18. @SJ_Chapman also stated that it is ethically essential to determine not only if biotechnology results in patient benefit, but also the mechanics of why it works and has benefit, and that this would require well-designed qualitative work and patient and public involvement to facilitate the future development of the technology. @DrJamesGlasbey raised the issues of learning curves, proctorship, and early outcomes reporting. @schnitzb added the problems of rushing a product into market based on inadequate data. Finally, @coezycoe suggested that value and cost would have an impact on patient access to new surgical technology.

“[We] need to avoid ‘try it, bin it’ attitudes when evaluating surgical [biotechnology].” @SJ_Chapman


Principle findings

The tweetchat reached a global audience across different surgical specialties, as well as attracting engagement from patients.

The form of surgical technology currently found to be the most useful among the tweetchat participants was preoperative imaging. Energy delivery systems were the most commonly used technology to assist with intraoperative bleeding. Most participants employed surgical technology to reduce the rates of surgical site infection. Exciting avenues for future innovation included navigation-guided surgery, increased use of the OR Black BoxTM, and developments in laparoscopic and robotic surgery. The use of 3D printing and AI were both considered to increase in the coming years, with potential advances in automation. There were numerous important ethical issues to consider when developing new surgical technology. Finally, the consensus among patients was that, while advances in surgical technology were welcome, they were not as important or valued as the surgeon who employs them.

Limitations of analytics

It should be noted that Twitter polls are unvalidated and subjected to selection bias, and one person may also control multiple accounts. Although some users provide information about their areas of expertise in their Twitter bios, this is unregulated, and they may not include conflicts of interest. There is therefore the risk of non-expert or uncited opinions being included in the synthesis. This caveat must be emphasised when sharing such data.

Some data may be confounded by “incidental retweeting”, whereby if a tweeter uses two hashtags together, it is not always possible to determine from which hashtag the retweet results. Third party social media tools tend to overestimate impressions and audience. Although Twitter Analytics may provide a more accurate measure of impressions, this is not possible to collect from a tweetchat in which multiple Twitter accounts are engaged. Finally, collecting such data is subject to the Hawthorne effect, where changes in behaviour may be affected by the act of observation 19.

To validate the findings, further research could incorporate validated methods of qualitative research, such as thematic analysis. 


Social media may be used to disseminate information within a vast surgical ecosystem, engaging surgeons with a strong social media presence. The use of a standardised hashtag in a tweetchat allows information to reach a high volume of global Twitter users in the surgical community in a short space of time. Tweetchats between a diverse group of surgeons, allied health professionals, and the general public, can be a goldmine for determining the direction of future surgical innovations.


The authors are grateful to the #SoMe4Surgery community for continued support.


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