JMIR Publications

ABSTRACT

Background:

Background:

Complications following abdominal surgery have a very significant negative impact on the patient and the health care system. Despite the spread of minimally invasive surgery, there is no automated way to use intraoperative video to predict complications. New developments in data storage capacity and artificial intelligence algorithm creation now allow for this opportunity.

Objective:

Objectives: This project aims to develop and validate deep learning models for accurately predicting postoperative complications, classified by the Clavien-Dindo scale. A key objective is to build and share an open-source dataset—containing both intraoperative video data and postoperative outcomes.

Methods:

Methods:

This prospective cohort study will collect data reflecting the day-to-day surgical practice will from 1200 patients, focusing on patient outcomes and intraoperative video. Data will be collected on patients submitted to minimally invasive appendectomy, cholecystectomy and colorectal resection in the urgent and elective setting. Each video will be annotated at the temporal and semantic level by the study team. Comprehensive data collection will encompass three domains: (1) preoperative variables including patient demographics, comorbidities, laboratory values, and imaging findings; (2) intraoperative data featuring complete surgical video recordings from laparoscopic/robotic monitors, procedure duration, surgical approach, intraoperative complications, and surgeon-defined technical factors; and (3) 30-day postoperative outcomes classified using the Clavien-Dindo scale (grades I-V).. This dataset will be shared under a non-commercial CC BY-NC-SA use license to promote scientific collaboration and innovation with complete anonymization including metadata removal and out-of-body image blurring. For analysis, the dataset will be split into a training, validation and a testing set. Deep learning algorithms will be developed through supervised learning methodology using two parallel approaches: data-derived predictors employing fine-tuned surgical video foundational models based on vision transformer architectures, and surgeon-defined predictors based on documented intraoperative strategies. Algorithms will be trained on the training set to predict the Clavien-Dindo postoperative complication grade and categorise postoperative outcomes in minimally invasive abdominal surgery. Model performance will be analysed through their respective sensitivity, specificity, positive and negative predictive values, and area under the receiver operating characteristic curve on the validation and testing sets.

Results:

Results:

Data collection started in 2024 and is expected to extend through 2025. The planned outputs include the publication of a research protocol, main results, and the open-source dataset. Through this initiative, the project seeks to significantly advance the field of artificial intelligence-assisted surgery, contributing to safer and more effective practice.

Conclusions:

Conclusions:

Through the creation of an open dataset and the development of state-of-the-art deep learning models, this project seeks to transform the current paradigm in minimally invasive surgery. By providing the surgical AI community with robust, real-world data, the project aspires to catalyse innovations that will enhance surgical safety, refine predictive capabilities, and ultimately lead to better clinical outcomes.