Measuring Decision‐Making During Thyroidectomy: Validity Evidence for a Web‐Based Assessment Tool

Madani, Amin; Gornitsky, Jordan; Watanabe, Yusuke; Benay, Cassandre; Altieri, Maria S.; Pucher, Philip H.; Tabah, Roger; Mitmaker, Elliot J.

doi:10.1007/s00268-017-4322-y

Cited by 10 publications

(9 citation statements)

References 41 publications

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“…Annotations were done using Think Like A Surgeon software (https://thinklikeasurgeon.ca). 15,16 While watching the source video for context, annotations are made on the still frames and selected pixels are submitted and mapped on the back-end of the platform. Details of the annotation process are provided in the Supplemental Digital Content (Appendix 1, http://links.lww.com/SLA/C727).…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence for Intraoperative Guidance

et al. 2020

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Objective: The aim of this study was to develop and evaluate the performance of artificial intelligence (AI) models that can identify safe and dangerous zones of dissection, and anatomical landmarks during laparoscopic cholecystectomy (LC). Summary Background Data: Many adverse events during surgery occur due to errors in visual perception and judgment leading to misinterpretation of anatomy. Deep learning, a subfield of AI, can potentially be used to provide real-time guidance intraoperatively. Methods: Deep learning models were developed and trained to identify safe (Go) and dangerous (No-Go) zones of dissection, liver, gallbladder, and hepatocystic triangle during LC. Annotations were performed by 4 high-volume surgeons. AI predictions were evaluated using 10-fold crossvalidation against annotations by expert surgeons. Primary outcomes were intersection-over-union (IOU) and F1 score (validated spatial correlation indices), and secondary outcomes were pixel-wise accuracy, sensitivity, specificity, ± standard deviation. Results: AI models were trained on 2627 random frames from 290 LC videos, procured from 37 countries, 136 institutions, and 153 surgeons. Mean IOU, F1 score, accuracy, sensitivity, and specificity for the AI to identify Go zones were 0.53 (±0.24), 0.70 (±0.28), 0.94 (±0.05), 0.69 (±0.20). and 0.94 (±0.03), respectively. For No-Go zones, these metrics were 0.71 (±0.29), 0.83 (±0.31), 0.95 (±0.06), 0.80 (±0.21), and 0.98 (±0.05), respectively. Mean IOU for identification of the liver, gallbladder, and hepatocystic triangle were: 0.86 (±0.12), 0.72 (±0.19), and 0.65 (±0.22), respectively. Conclusions: AI can be used to identify anatomy within the surgical field. This technology may eventually be used to provide real-time guidance and minimize the risk of adverse events.

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Section: Methodsmentioning

confidence: 99%

Artificial Intelligence for Intraoperative Guidance

et al. 2020

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“…The output was a heatmap on each frame of the surgical footage, with each pixel denoted as either Go or No-Go based on the percentage of surgeons that classified it as such. 32 AI tools have thus been developed to interpret timeseries data in real time during surgery beyond human abilities and help automate an otherwise labor-intensive process. Other tools can facilitate the analysis of surgical footage to augment surgical experience and improve trainee education as further elaborated in our article on advancing surgical education through AI application.…”

Section: Perioperative Applicationsmentioning

confidence: 99%

“…The output was a heatmap on each frame of the surgical footage, with each pixel denoted as either Go or No-Go based on the percentage of surgeons that classified it as such. 32…”

Section: Introductionmentioning

confidence: 99%

A Synopsis of Artificial Intelligence and its Applications in Surgery

Rajesh

Chartier²,

Asaad

et al. 2022

The American Surgeon

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Artificial intelligence (AI) has made steady in-roads into the healthcare scenario over the last decade. While widespread adoption into clinical practice remains elusive, the outreach of this discipline has progressed beyond the physician scientist, and different facets of this technology have been incorporated into the care of surgical patients. New AI applications are developing at rapid pace, and it is imperative that the general surgeon be aware of the broad utility of AI as applicable in his or her day-to-day practice, so that healthcare continues to remain up-to-date and evidence based. This review provides a broad account of the tip of the AI iceberg and highlights it potential for positively impacting surgical care.

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“…This form of training by using visual concordance of annotations on a surgical canvas has shown to be a very promising method of improving intraoperative judgment. 18 , 19 …”

Section: Videoconferencingmentioning

confidence: 99%

Leveraging Videoconferencing Technology to Augment Surgical Training During a Pandemic

Madani

Hirpara

Chadi

et al. 2021

Annals of Surgery Open

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Objective: Our objective was to review the use of videoconferencing as a practical tool for remote surgical education and to propose a model to overcome the impact of a pandemic on resident training. Summary Background Data: In response to the coronavirus disease 2019 pandemic, most institutions and residency programs have been restructured to minimize the number of residents in the hospital as well as their interactions with patients and to promote physical distancing measures. This has resulted in decreased resident operative exposure, responsibility, and autonomy, hindering their educational goals and ability to achieve surgical expertise necessary for independent practice. Methods: We conducted a narrative review to explore the use of videoconferencing for remote broadcasting of surgical procedures, telecoaching using surgical videos, telesimulation for surgical skills training, and establishing a didactic lecture series. Results and Conclusions: We present a multimodal approach for using practical videoconferencing tools that provide the means for audiovisual communication to help augment residents’ operative experience and limit the impact of self-isolation, redeployment, and limited operative exposure on surgical training.

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Measuring Decision‐Making During Thyroidectomy: Validity Evidence for a Web‐Based Assessment Tool

Abstract: This study describes the development of novel metrics and provides validity evidence for an interactive Web-based platform to objectively assess decision-making during thyroidectomy.

Cited by 10 publications

References 41 publications

Artificial Intelligence for Intraoperative Guidance

Artificial Intelligence for Intraoperative Guidance

A Synopsis of Artificial Intelligence and its Applications in Surgery

Leveraging Videoconferencing Technology to Augment Surgical Training During a Pandemic

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