A novel high accuracy model for automatic surgical workflow recognition using artificial intelligence in laparoscopic totally extraperitoneal inguinal hernia repair (TEP)

Machine learning, deep learning and hernia surgery. Are we pushing the limits of abdominal core health? A qualitative systematic review

INTRODUCTION

This systematic review aims to evaluate the use of machine learning and artificial intelligence in hernia surgery.

METHODS

The PRISMA guidelines were followed throughout this systematic review. The ROBINS-I and Rob 2 tools were used to perform qualitative assessment of all studies included in this review. Recommendations were then summarized for the following pre-defined key items: protocol, research question, search strategy, study eligibility, data extraction, study design, risk of bias, publication bias, and statistical analysis.

RESULTS

A total of 13 articles were ultimately included for this review, describing the use of machine learning and deep learning for hernia surgery. All studies were published from 2020 to 2023. Articles varied regarding the population studied, type of machine learning or Deep Learning Model (DLM) used, and hernia type. Of the thirteen included studies, all included either inguinal, ventral, or incisional hernias. Four studies evaluated recognition of surgical steps during inguinal hernia repair videos. Two studies predicted outcomes using image-based DMLs. Seven studies developed and validated deep learning algorithms to predict outcomes and identify factors associated with postoperative complications.

CONCLUSION

The use of ML for abdominal wall reconstruction has been shown to be a promising tool for predicting outcomes and identifying factors that could lead to postoperative complications.

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

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.

Convolutional Neural Network-Based Deep Learning Engine for Mastoidectomy Instrument Recognition and Movement Tracking

OBJECTIVE

To develop a convolutional neural network-based computer vision model to recognize and track 2 mastoidectomy surgical instruments-the drill and the suction-irrigator-from intraoperative video recordings of mastoidectomies.

STUDY DESIGN

Technological development and model validation.

SETTING

Academic center.

METHODS

Ten 1-minute videos of mastoidectomies done for cochlear implantation by varying levels of resident surgeons were collected. For each video, containing 900 frames, an open-access computer vision annotation tool was used to annotate the drill and suction-irrigator class images with bounding boxes. A mastoidectomy instrument tracking module, which extracts the center coordinates of bounding boxes, was developed using a feature pyramid network and layered with DETECTRON, an open-access faster-region-based convolutional neural network. Eight videos were used to train the model, and 2 videos were used for testing. Outcome measures included Intersection over Union (IoU) ratio, accuracy, and average precision.

RESULTS

For an IoU of 0.5, the mean average precision for the drill was 99% and 86% for the suction-irrigator. The model proved capable of generating maps of drill and suction-irrigator stroke direction and distance for the entirety of each video.

CONCLUSIONS

This computer vision model can identify and track the drill and suction-irrigator from videos of intraoperative mastoidectomies performed by residents with excellent precision. It can now be employed to retrospectively study objective mastoidectomy measures of expert and resident surgeons, such as drill and suction-irrigator stroke concentration, economy of motion, speed, and coordination, setting the stage for characterization of objective expectations for safe and efficient mastoidectomies.

Implementation of Artificial Intelligence-Based Computer Vision Model for Sleeve Gastrectomy: Experience in One Tertiary Center

INTRODUCTION

Sleeve gastrectomy (SG) is the most common metabolic and bariatric procedure performed. Leveraging artificial intelligence (AI) for automated real-time data structuring and annotations of surgical videos has immense potential of clinical applications. This study presents initial real-world implementation of AI-based computer vision model in sleeve gastrectomy (SG) and external validation of accuracy of safety milestone annotations.

METHODS

A retrospective single-center study of 49 consecutive SG videos was captured and analyzed by the AI platform (December 2020-August 2023). A bariatric surgeon viewed all videos and assessed safety milestones adherence, compared to the AI annotations. Patients' data were retrieved from the bariatric unit registry.

RESULTS

SG total duration was 47.5 min (interquartile range 36-64). Main steps included preparation (12.2%), dissection of the greater curvature (30.8%), gastric transection (28.5%), specimen extraction (7.2%), and final inspection (14.4%). Out of body time comprised 6.9% of the total video. Safety milestones components and AI-surgeon agreements included the following: bougie insertion (100%), distance from pylorus ≥ 2 cm (100%), parallel to lesser curvature (98%), fundus mobilization (100%), and distance from esophagus ≥ 1 cm (true-100%, false-13.6%; kappa coefficient 0.2, p = 0.006). Intraoperative complications included notable hemorrhage (n = 4) and parenchymal injury (n = 1).

CONCLUSIONS

The AI model provides a fully automated SG video analysis. Outcomes suggest its accuracy in four of five safety milestone annotations. This data is valuable, as it reflects objective performance measures which can help us improve the surgical quality and efficiency of SG. Larger cohorts will enable SG standardization and clinical correlations with outcomes, aiming to improve patients' safety.