Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Surgical Endoscopy

25-04-2024 | Artificial Intelligence

Implementation of artificial intelligence-based computer vision model in laparoscopic appendectomy: validation, reliability, and clinical correlation

Authors: Danit Dayan, Nadav Dvir, Haneen Agbariya, Eran Nizri

Published in: Surgical Endoscopy

Login to get access

Abstract

Background

Application of artificial intelligence (AI) in general surgery is evolving. Real-world implementation of an AI-based computer-vision model in laparoscopic appendectomy (LA) is presented. We aimed to evaluate (1) its accuracy in complexity grading and safety adherence, (2) clinical correlation to outcomes.

Methods

A retrospective single-center study of 499 consecutive LA videos, captured and analyzed by ‘Surgical Intelligence Platform,’ Theator Inc. (9/2020–5/2022). Two expert surgeons viewed all videos and manually graded complexity and safety adherence. Automated annotations were compared to surgeons’ assessments. Inter-surgeons’ agreements were measured. Since 7/2021 videos were linked to patients’ admission numbers. Data retrieval from medical records was performed (n = 365). Outcomes were compared between high and low complexity grades.

Results

Low and high complexity grades comprised 74.8 and 25.2% of 499 videos. Surgeons’ agreements were high (76.9–94.4%, kappa 0.77/0.91; p < 0.001) for all annotated complexity grades. Surgeons’ agreements were also high (96.0–99.8%, kappa 0.78/0.87; p < 0.001) for full safety adherence, whereas agreement was moderate in partial safety adherence and none (32.8–58.8%). Inter-surgeons’ agreements were high for complexity grading (kappa 0.86, p < 0.001) and safety adherence (kappa 0.88, p < 0.001). Comparing high to low grade complexity, preoperative clinical features were similar, except larger appendix diameter on imaging (13.4 ± 4.4 vs. 10.5 ± 3.0 mm, p < 0.001). Intraoperative outcomes were significantly higher (p < 0.001), including time to achieve critical view of safety (29.6, IQR 19.1–41.6 vs. 13.7, IQR 8.5–21.1 min), operative duration (45.3, IQR 37.7–65.2 vs. 25.0, IQR 18.3–32.7 min), and intraoperative events (39.4% vs. 5.9%). Postoperative outcomes (7.4% vs. 9.2%) including surgical complications, mortality, and readmissions were comparable (p = 0.6), except length of stay (4, IQR 2–5.5 vs. 1, IQR 1-2 days; p < 0.001).

Conclusion

The model accurately assesses complexity grading and full safety achievement. It can serve to predict operative time and intraoperative course, whereas no clinical correlation was found regarding postoperative outcomes. Further studies are needed.
Literature
1.
Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G (2020) Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations. Anesthesiology 132:379–394CrossRefPubMed
2.
Hunter B, Hindocha S, Lee RW (2022) The role of artificial intelligence in early cancer diagnosis. Cancers (Basel) 14:1524CrossRefPubMed
3.
Wang S, Cao G, Wang Y, Liao S, Wang Q, Shi J, Li C, Shen D (2021) Review and prospect: artificial intelligence in advanced medical imaging. Front Radiol 1:781868CrossRefPubMedPubMedCentral
4.
Hashimoto DA, Rosman G, Rus D, Meireles OR (2018) Artificial intelligence in surgery: promises and perils. Ann Surg 268:70–76CrossRefPubMed
5.
6.
Ahmadi SA, Sielhorst T, Stauder R, Horn M, Feussner H, Navab N (2006) Recovery of surgical workflow without explicit models. Med Image Comput Comput Assist Interv 9:420–428PubMed
7.
Garrow CR, Kowalewski KF, Li L, Wagner M, Schmidt MW, Engelhardt S, Hashimoto DA, Kenngott HG, Bodenstedt S, Speidel S, Muller-Stich BP, Nickel F (2021) Machine learning for surgical phase recognition: a systematic review. Ann Surg 273:684–693CrossRefPubMed
8.
Nyangoh Timoh K, Huaulme A, Cleary K, Zaheer MA, Lavoue V, Donoho D, Jannin P (2023) A systematic review of annotation for surgical process model analysis in minimally invasive surgery based on video. Surg Endosc 37:4298–4314CrossRefPubMed
9.
Korndorffer JR Jr, Hawn MT, Spain DA, Knowlton LM, Azagury DE, Nassar AK, Lau JN, Arnow KD, Trickey AW, Pugh CM (2020) Situating artificial intelligence in surgery: a focus on disease severity. Ann Surg 272:523–528CrossRefPubMed
10.
Ortenzi M, Rapoport Ferman J, Antolin A, Bar O, Zohar M, Perry O, Asselmann D, Wolf T (2023) A novel high accuracy model for automatic surgical workflow recognition using artificial intelligence in laparoscopic totally extraperitoneal inguinal hernia repair (TEP). Surg Endosc 37:8818–8828CrossRefPubMedPubMedCentral
11.
12.
O’Leary DP, Walsh SM, Bolger J, Baban C, Humphreys H, O’Grady S, Hegarty A, Lee AM, Sheehan M, Alderson J, Dunne R, Morrin MM, Lee MJ, Power C, McNamara D, McCawley N, Robb W, Burke J, Sorensen J, Hill AD (2021) A randomized clinical trial evaluating the efficacy and quality of life of antibiotic-only treatment of acute uncomplicated appendicitis: results of the COMMA trial. Ann Surg 274:240–247CrossRefPubMed
13.
Subramanian A, Liang MK (2012) A 60-year literature review of stump appendicitis: the need for a critical view. Am J Surg 203:503–507CrossRefPubMed
14.
15.
Gomes CA, Nunes TA, Fonseca Chebli JM, Junior CS, Gomes CC (2012) Laparoscopy grading system of acute appendicitis: new insight for future trials. Surg Laparosc Endosc Percutan Tech 22:463–466CrossRefPubMed
16.
17.
Laxague F, Angeramo CA, Schlottmann F (2021) Development and validation of a novel nomogram to predict the risk of postoperative intraabdominal abscess after laparoscopic appendectomy. J Gastrointest Surg 25:2101–2103CrossRefPubMed
18.
Bar O, Neimark D, Zohar M, Hager GD, Girshick R, Fried GM, Wolf T, Asselmann D (2020) Impact of data on generalization of AI for surgical intelligence applications. Sci Rep 10:22208CrossRefPubMedPubMedCentral
19.
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2021) An image is worth 16 × 16 words: transformers for image recognition at scale. arXiv
20.
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press
21.
Zohar M, Bar O, Neimark D, Hager GD, Asselmann D (2020) Accurate detection of out of body segments in surgical video using semi-supervised learning. Proceedings of the 3rd conference on medical imaging with deep learning, pp 923–936
22.
Neimark D, Bar O, Zohar M, Asselmann D (2021) Video trans-former network. Proceedings of the IEEE/CVF international conference on computer vision, pp 3163–3172
23.
Ferris M, Quan S, Kaplan BS, Molodecky N, Ball CG, Chernoff GW, Bhala N, Ghosh S, Dixon E, Ng S, Kaplan GG (2017) The global incidence of appendicitis: a systematic review of population-based studies. Ann Surg 266:237–241CrossRefPubMed
24.
Moris D, Paulson EK, Pappas TN (2021) Diagnosis and management of acute appendicitis in adults: a review. JAMA 326:2299–2311CrossRefPubMed
25.
26.
27.
Strasberg SM, Hertl M, Soper NJ (1995) An analysis of the problem of biliary injury during laparoscopic cholecystectomy. J Am Coll Surg 180:101–125PubMed
28.
Mascagni P, Vardazaryan A, Alapatt D, Urade T, Emre T, Fiorillo C, Pessaux P, Mutter D, Marescaux J, Costamagna G, Dallemagne B, Padoy N (2022) Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Ann Surg 275:955–961CrossRefPubMed
29.
de Wijkerslooth EML, Boerma EG, van Rossem CC, van Rosmalen J, Baeten CIM, Beverdam FH, Bosmans J, Consten ECJ, Dekker JWT, Emous M, van Geloven AAW, Gijsen AF, Heijnen LA, Jairam AP, Melles DC, van der Ploeg APT, Steenvoorde P, Toorenvliet BR, Vermaas M, Wiering B, Wijnhoven BPL, van den Boom AL (2023) 2 days versus 5 days of postoperative antibiotics for complex appendicitis: a pragmatic, open-label, multicentre, non-inferiority randomised trial. Lancet 401:366–376CrossRefPubMed
30.
31.
Patel SV, Zhang L, Mir ZM, Lemke M, Leeper WR, Allen LJ, Walser E, Vogt K (2023) Delayed vs. early laparoscopic appendectomy (DELAY) for adult patients with acute appendicitis: a randomized controlled trial. Ann Surg 279:88CrossRefPubMed
Metadata
Title
Implementation of artificial intelligence-based computer vision model in laparoscopic appendectomy: validation, reliability, and clinical correlation
Authors
Danit Dayan
Nadav Dvir
Haneen Agbariya
Eran Nizri
Publication date
25-04-2024
Publisher
Springer US
Published in
Surgical Endoscopy
Print ISSN: 0930-2794
Electronic ISSN: 1432-2218
DOI
https://doi.org/10.1007/s00464-024-10847-2