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Article ; Online: Introducing surgical intelligence in gynecology: Automated identification of key steps in hysterectomy.

Levin, Ishai / Rapoport Ferman, Judith / Bar, Omri / Ben Ayoun, Danielle / Cohen, Aviad / Wolf, Tamir

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics

2024

Abstract: Objective: The analysis of surgical videos using artificial intelligence holds great promise for the future of surgery by facilitating the development of surgical best practices, identifying key pitfalls, enhancing situational awareness, and ... ...

Abstract	Objective: The analysis of surgical videos using artificial intelligence holds great promise for the future of surgery by facilitating the development of surgical best practices, identifying key pitfalls, enhancing situational awareness, and disseminating that information via real-time, intraoperative decision-making. The objective of the present study was to examine the feasibility and accuracy of a novel computer vision algorithm for hysterectomy surgical step identification. Methods: This was a retrospective study conducted on surgical videos of laparoscopic hysterectomies performed in 277 patients in five medical centers. We used a surgical intelligence platform (Theator Inc.) that employs advanced computer vision and AI technology to automatically capture video data during surgery, deidentify, and upload procedures to a secure cloud infrastructure. Videos were manually annotated with sequential steps of surgery by a team of annotation specialists. Subsequently, a computer vision system was trained to perform automated step detection in hysterectomy. Analyzing automated video annotations in comparison to manual human annotations was used to determine accuracy. Results: The mean duration of the videos was 103 ± 43 min. Accuracy between AI-based predictions and manual human annotations was 93.1% on average. Accuracy was highest for the dissection and mobilization step (96.9%) and lowest for the adhesiolysis step (70.3%). Conclusion: The results of the present study demonstrate that a novel AI-based model achieves high accuracy for automated steps identification in hysterectomy. This lays the foundations for the next phase of AI, focused on real-time clinical decision support and prediction of outcome measures, to optimize surgeon workflow and elevate patient care.
Language	English
Publishing date	2024-03-28
Publishing country	United States
Document type	Journal Article
ZDB-ID	80149-5
ISSN	1879-3479 ; 0020-7292
ISSN (online)	1879-3479
ISSN	0020-7292
DOI	10.1002/ijgo.15490
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Article ; Online: Response to the Comments on "Situating Artificial Intelligence in Surgery, a Focus on Disease Severity".

Pugh, Carla M / Wolf, Tamir / Korndorffer, James R

Annals of surgery

2021 Volume 274, Issue 6, Page(s) e892–e893

MeSH term(s)	Artificial Intelligence ; Humans ; Severity of Illness Index
Language	English
Publishing date	2021-02-20
Publishing country	United States
Document type	Letter ; Comment
ZDB-ID	340-2
ISSN	1528-1140 ; 0003-4932
ISSN (online)	1528-1140
ISSN	0003-4932
DOI	10.1097/SLA.0000000000004820
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Article ; Online: A novel high accuracy model for automatic surgical workflow recognition using artificial intelligence in laparoscopic totally extraperitoneal inguinal hernia repair (TEP).

Ortenzi, Monica / Rapoport Ferman, Judith / Antolin, Alenka / Bar, Omri / Zohar, Maya / Perry, Ori / Asselmann, Dotan / Wolf, Tamir

Surgical endoscopy

2023 Volume 37, Issue 11, Page(s) 8818–8828

Abstract: Introduction: Artificial intelligence and computer vision are revolutionizing the way we perceive video analysis in minimally invasive surgery. This emerging technology has increasingly been leveraged successfully for video segmentation, documentation, ... ...

Abstract	Introduction: Artificial intelligence and computer vision are revolutionizing the way we perceive video analysis in minimally invasive surgery. This emerging technology has increasingly been leveraged successfully for video segmentation, documentation, education, and formative assessment. New, sophisticated platforms allow pre-determined segments chosen by surgeons to be automatically presented without the need to review entire videos. This study aimed to validate and demonstrate the accuracy of the first reported AI-based computer vision algorithm that automatically recognizes surgical steps in videos of totally extraperitoneal (TEP) inguinal hernia repair. Methods: Videos of TEP procedures were manually labeled by a team of annotators trained to identify and label surgical workflow according to six major steps. For bilateral hernias, an additional change of focus step was also included. The videos were then used to train a computer vision AI algorithm. Performance accuracy was assessed in comparison to the manual annotations. Results: A total of 619 full-length TEP videos were analyzed: 371 were used to train the model, 93 for internal validation, and the remaining 155 as a test set to evaluate algorithm accuracy. The overall accuracy for the complete procedure was 88.8%. Per-step accuracy reached the highest value for the hernia sac reduction step (94.3%) and the lowest for the preperitoneal dissection step (72.2%). Conclusions: These results indicate that the novel AI model was able to provide fully automated video analysis with a high accuracy level. High-accuracy models leveraging AI to enable automation of surgical video analysis allow us to identify and monitor surgical performance, providing mathematical metrics that can be stored, evaluated, and compared. As such, the proposed model is capable of enabling data-driven insights to improve surgical quality and demonstrate best practices in TEP procedures.
MeSH term(s)	Humans ; Hernia, Inguinal/surgery ; Laparoscopy/methods ; Artificial Intelligence ; Workflow ; Minimally Invasive Surgical Procedures ; Herniorrhaphy/methods ; Surgical Mesh
Language	English
Publishing date	2023-08-25
Publishing country	Germany
Document type	Journal Article
ZDB-ID	639039-0
ISSN	1432-2218 ; 0930-2794
ISSN (online)	1432-2218
ISSN	0930-2794
DOI	10.1007/s00464-023-10375-5
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Article ; Online: Automated Identification of Key Steps in Robotic-Assisted Radical Prostatectomy Using Artificial Intelligence.

Khanna, Abhinav / Antolin, Alenka / Bar, Omri / Ben-Ayoun, Danielle / Zohar, Maya / Boorjian, Stephen A / Frank, Igor / Shah, Paras / Sharma, Vidit / Thompson, R Houston / Wolf, Tamir / Asselmann, Dotan / Tollefson, Matthew

The Journal of urology

2024 Volume 211, Issue 4, Page(s) 575–584

Abstract: Purpose: The widespread use of minimally invasive surgery generates vast amounts of potentially useful data in the form of surgical video. However, raw video footage is often unstructured and unlabeled, thereby limiting its use. We developed a novel ... ...

Abstract	Purpose: The widespread use of minimally invasive surgery generates vast amounts of potentially useful data in the form of surgical video. However, raw video footage is often unstructured and unlabeled, thereby limiting its use. We developed a novel computer-vision algorithm for automated identification and labeling of surgical steps during robotic-assisted radical prostatectomy (RARP). Materials and methods: Surgical videos from RARP were manually annotated by a team of image annotators under the supervision of 2 urologic oncologists. Full-length surgical videos were labeled to identify all steps of surgery. These manually annotated videos were then utilized to train a computer vision algorithm to perform automated video annotation of RARP surgical video. Accuracy of automated video annotation was determined by comparing to manual human annotations as the reference standard. Results: A total of 474 full-length RARP videos (median 149 minutes; IQR 81 minutes) were manually annotated with surgical steps. Of these, 292 cases served as a training dataset for algorithm development, 69 cases were used for internal validation, and 113 were used as a separate testing cohort for evaluating algorithm accuracy. Concordance between artificial intelligence‒enabled automated video analysis and manual human video annotation was 92.8%. Algorithm accuracy was highest for the vesicourethral anastomosis step (97.3%) and lowest for the final inspection and extraction step (76.8%). Conclusions: We developed a fully automated artificial intelligence tool for annotation of RARP surgical video. Automated surgical video analysis has immediate practical applications in surgeon video review, surgical training and education, quality and safety benchmarking, medical billing and documentation, and operating room logistics.
MeSH term(s)	Humans ; Male ; Artificial Intelligence ; Educational Status ; Prostate/surgery ; Prostatectomy/methods ; Robotic Surgical Procedures/methods ; Video Recording
Language	English
Publishing date	2024-01-24
Publishing country	United States
Document type	Journal Article
ZDB-ID	3176-8
ISSN	1527-3792 ; 0022-5347
ISSN (online)	1527-3792
ISSN	0022-5347
DOI	10.1097/JU.0000000000003845
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Article ; Online: Automated surgical step recognition in transurethral bladder tumor resection using artificial intelligence: transfer learning across surgical modalities.

Deol, Ekamjit S / Tollefson, Matthew K / Antolin, Alenka / Zohar, Maya / Bar, Omri / Ben-Ayoun, Danielle / Mynderse, Lance A / Lomas, Derek J / Avant, Ross A / Miller, Adam R / Elliott, Daniel S / Boorjian, Stephen A / Wolf, Tamir / Asselmann, Dotan / Khanna, Abhinav

Frontiers in artificial intelligence

2024 Volume 7, Page(s) 1375482

Abstract: Objective: Automated surgical step recognition (SSR) using AI has been a catalyst in the "digitization" of surgery. However, progress has been limited to laparoscopy, with relatively few SSR tools in endoscopic surgery. This study aimed to create a SSR ... ...

Abstract	Objective: Automated surgical step recognition (SSR) using AI has been a catalyst in the "digitization" of surgery. However, progress has been limited to laparoscopy, with relatively few SSR tools in endoscopic surgery. This study aimed to create a SSR model for transurethral resection of bladder tumors (TURBT), leveraging a novel application of transfer learning to reduce video dataset requirements. Materials and methods: Retrospective surgical videos of TURBT were manually annotated with the following steps of surgery: primary endoscopic evaluation, resection of bladder tumor, and surface coagulation. Manually annotated videos were then utilized to train a novel AI computer vision algorithm to perform automated video annotation of TURBT surgical video, utilizing a transfer-learning technique to pre-train on laparoscopic procedures. Accuracy of AI SSR was determined by comparison to human annotations as the reference standard. Results: A total of 300 full-length TURBT videos (median 23.96 min; IQR 14.13-41.31 min) were manually annotated with sequential steps of surgery. One hundred and seventy-nine videos served as a training dataset for algorithm development, 44 for internal validation, and 77 as a separate test cohort for evaluating algorithm accuracy. Overall accuracy of AI video analysis was 89.6%. Model accuracy was highest for the primary endoscopic evaluation step (98.2%) and lowest for the surface coagulation step (82.7%). Conclusion: We developed a fully automated computer vision algorithm for high-accuracy annotation of TURBT surgical videos. This represents the first application of transfer-learning from laparoscopy-based computer vision models into surgical endoscopy, demonstrating the promise of this approach in adapting to new procedure types.
Language	English
Publishing date	2024-03-07
Publishing country	Switzerland
Document type	Journal Article
ISSN	2624-8212
ISSN (online)	2624-8212
DOI	10.3389/frai.2024.1375482
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Article: Evaluating new types of tourniquets by the Israeli Naval special warfare unit.

Heldenberg, Eitan / Aharony, Shahar / Wolf, Tamir / Vishne, Tali

Disaster and military medicine

2015 Volume 1, Page(s) 1

Abstract: Background: Extremity injuries, which accounts for 20% of all battlefield injuries, result in 7-9% of deaths during military activity. Silicone tourniquets were used, by the Israeli Defense Force (IDF) soldiers, for upper extremity and calf injuries, ... ...

Abstract	Background: Extremity injuries, which accounts for 20% of all battlefield injuries, result in 7-9% of deaths during military activity. Silicone tourniquets were used, by the Israeli Defense Force (IDF) soldiers, for upper extremity and calf injuries, while thigh injuries were treated by an improvised "Russian" tourniquet (IRT). This is the first study, performed in the IDF, comparing the IRT with Combat Application Tourniquets (CAT) and Special Operations Force Tactical Tourniquets (SOFTT). 23 operators from the Israeli Naval Unit (Shayetet 13) were divided into two groups according to their medical training (11 operators trained as first-responders; 12 operators as medics). Repetitive applications of the three tourniquets over the thigh and upper arm, and self-application of the CAT and SOFTT over the dominant extremity were performed using dry and wet tourniquets (828 individual placements) with efficacy recorded. Cessation of distal arterial flow (palpation; Doppler ultrasound) confirmed success, while failure was considered in the advent of arterial flow or tourniquet instability. Satisfaction questionnaires were filled by the operators. Results: CAT and SOFTT were found to be superior to the IRT, in occluding arterial blood flow to the extremities (22%, 23% and 38%, respectively, failure rate). The application was quicker for the CAT and SOFTT as compared to the IRT (18, 26, 52 seconds, respectively). Wet tourniquets neither prolonged application nor did they increase failure rates. Similarly, medics didn't have any advantage over non-medic operators. No findings indicated superiority of CAT and SOFTT over one another, despite operators' preference of CAT. Conclusions: CAT and SOFTT offer an effective alternative to the IRT in stopping blood flow to extremities. No difference was observed between medics and non-medic operators. Thus, the CAT was elected as the preferred tourniquet by our unit and it is being used by all the operators.
Language	English
Publishing date	2015-01-27
Publishing country	England
Document type	Journal Article
ISSN	2054-314X
ISSN	2054-314X
DOI	10.1186/2054-314X-1-1
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Article ; Online: Impact of data on generalization of AI for surgical intelligence applications.

Bar, Omri / Neimark, Daniel / Zohar, Maya / Hager, Gregory D / Girshick, Ross / Fried, Gerald M / Wolf, Tamir / Asselmann, Dotan

Scientific reports

2020 Volume 10, Issue 1, Page(s) 22208

Abstract: AI is becoming ubiquitous, revolutionizing many aspects of our lives. In surgery, it is still a promise. AI has the potential to improve surgeon performance and impact patient care, from post-operative debrief to real-time decision support. But, how much ...

Abstract	AI is becoming ubiquitous, revolutionizing many aspects of our lives. In surgery, it is still a promise. AI has the potential to improve surgeon performance and impact patient care, from post-operative debrief to real-time decision support. But, how much data is needed by an AI-based system to learn surgical context with high fidelity? To answer this question, we leveraged a large-scale, diverse, cholecystectomy video dataset. We assessed surgical workflow recognition and report a deep learning system, that not only detects surgical phases, but does so with high accuracy and is able to generalize to new settings and unseen medical centers. Our findings provide a solid foundation for translating AI applications from research to practice, ushering in a new era of surgical intelligence.
Language	English
Publishing date	2020-12-17
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-020-79173-6
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Article ; Online: Israel-Gaza conflict.

Wolf, Tamir / Brown, Danielle H / Aharony, Shachar M

Lancet (London, England)

2014 Volume 384, Issue 9942, Page(s) 489–490

MeSH term(s)	Humans ; Warfare
Language	English
Publishing date	2014-08-09
Publishing country	England
Document type	Comment ; Letter
ZDB-ID	3306-6
ISSN	1474-547X ; 0023-7507 ; 0140-6736
ISSN (online)	1474-547X
ISSN	0023-7507 ; 0140-6736
DOI	10.1016/S0140-6736(14)61126-0
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Article ; Online: Harmonic scalpel versus flexible CO2 laser for tongue resection

Wolf Tamir / Whitworth Richard / Loehn Bridget / Zieske Arthur W / Gremillion Grayson / Hanby Duncan F / Kakade Anagha C / Walvekar Rohan R

World Journal of Surgical Oncology, Vol 9, Iss 1, p

A histopathological analysis of thermal damage in human cadavers

2011 Volume 83

Abstract: Abstract Background Monopolar cautery is the most commonly used surgical cutting and hemostatic tool for head and neck surgery. There are newer technologies that are being utilized with the goal of precise cutting, decreasing blood loss, reducing thermal ...

Abstract	Abstract Background Monopolar cautery is the most commonly used surgical cutting and hemostatic tool for head and neck surgery. There are newer technologies that are being utilized with the goal of precise cutting, decreasing blood loss, reducing thermal damage, and allowing faster wound healing. Our study compares thermal damage caused by Harmonic scalpel and CO2 laser to cadaveric tongue. Methods Two fresh human cadaver heads were enrolled for the study. Oral tongue was exposed and incisions were made in the tongue akin to a tongue tumor resection using the harmonic scalpel and flexible C02 laser fiber at various settings recommended for surgery. The margins of resection were sampled, labeled, and sent for pathological analysis to assess depth of thermal damage calculated in millimeters. The pathologist was blinded to the surgical tool used. Control tongue tissue was also sent for comparison as a baseline for comparison. Results Three tongue samples were studied to assess depth of thermal damage by harmonic scalpel. The mean depth of thermal damage was 0.69 (range, 0.51 - 0.82). Five tongue samples were studied to assess depth of thermal damage by CO2 laser. The mean depth of thermal damage was 0.3 (range, 0.22 to 0.43). As expected, control samples showed 0 mm of thermal damage. There was a statistically significant difference between the depth of thermal injury to tongue resection margins by harmonic scalpel as compared to CO2 laser, (p = 0.003). Conclusion In a cadaveric model, flexible CO2 laser fiber causes less depth of thermal damage when compared with harmonic scalpel at settings utilized in our study. However, the relevance of this information in terms of wound healing, hemostasis, safety, cost-effectiveness, and surgical outcomes needs to be further studied in clinical settings.
Keywords	Neoplasms. Tumors. Oncology. Including cancer and carcinogens ; RC254-282 ; Internal medicine ; RC31-1245 ; Medicine ; R ; DOAJ:Oncology ; DOAJ:Medicine (General) ; DOAJ:Health Sciences
Subject code	670
Language	English
Publishing date	2011-08-01T00:00:00Z
Publisher	BioMed Central
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Application of a flexible CO(2) laser fiber for neurosurgery: laser-tissue interactions.

Ryan, Robert W / Wolf, Tamir / Spetzler, Robert F / Coons, Stephen W / Fink, Yoel / Preul, Mark C

Journal of neurosurgery

2010 Volume 112, Issue 2, Page(s) 434–443

Abstract: Object: The CO(2) laser has an excellent profile for use in neurosurgery. Its high absorption in water results in low thermal spread, sparing adjacent tissue. Use of this laser has been limited to line-of-sight applications because no solid fiber optic ... ...

Abstract	Object: The CO(2) laser has an excellent profile for use in neurosurgery. Its high absorption in water results in low thermal spread, sparing adjacent tissue. Use of this laser has been limited to line-of-sight applications because no solid fiber optic cables could transmit its wavelength. Flexible photonic bandgap fiber technology enables delivery of CO(2) laser energy through a flexible fiber easily manipulated in a handheld device. The authors examined and compared the first use of this CO(2) laser fiber to conventional methods for incising neural tissue. Methods: Carbon dioxide laser energy was delivered in pulsed or continuous wave settings for different power settings, exposure times, and distances to cortical tissue of 6 anesthetized swine. Effects of CO(2) energy on the tissue were compared with bipolar cautery using a standard pial incision technique, and with scalpel incisions without cautery. Tissue was processed for histological analysis (using H & E, silver staining, and glial fibrillary acidic protein immunohistochemistry) and scanning electron microscopy, and lesion measurements were made. Results: Light microscopy and scanning electron microscopy revealed laser incisions of consistent shape, with central craters surrounded by limited zones of desiccated and edematous tissue. Increased laser power resulted in deeper but not significantly wider incisions. Bipolar cautery lesions showed desiccated and edematous zones but did not incise the pia, and width increased more than depth with higher power. Incisions made without using cautery produced hemorrhage but minimal adjacent tissue damage. Conclusions: The photonic bandgap fiber CO(2) laser produced reliable cortical incisions, adjustable over a range of settings, with minimal adjacent thermal tissue damage. Ease of application under the microscope suggests this laser system has reached true practicality for neurosurgery.
MeSH term(s)	Animals ; Cautery/methods ; Cerebral Cortex/metabolism ; Cerebral Cortex/pathology ; Cerebral Cortex/surgery ; Desiccation ; Edema/etiology ; Female ; Fiber Optic Technology/instrumentation ; Fiber Optic Technology/methods ; Glial Fibrillary Acidic Protein/metabolism ; Immunohistochemistry ; Lasers, Gas/therapeutic use ; Microscopy, Electron, Scanning ; Neurosurgical Procedures/instrumentation ; Neurosurgical Procedures/methods ; Photomicrography ; Pia Mater/metabolism ; Pia Mater/pathology ; Pia Mater/surgery ; Swine
Chemical Substances	Glial Fibrillary Acidic Protein
Language	English
Publishing date	2010-02
Publishing country	United States
Document type	Comparative Study ; Journal Article
ZDB-ID	3089-2
ISSN	1933-0693 ; 0022-3085
ISSN (online)	1933-0693
ISSN	0022-3085
DOI	10.3171/2009.7.JNS09356
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