SynthesisSurgical endoscopy2021
Deep learning visual analysis in laparoscopic surgery: a systematic review and diagnostic test accuracy meta-analysis.
Synthesis in Surgical endoscopy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 7 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
50 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Artificial intelligence in the operating room: A systematic review of AI models for surgical phase, instruments and anatomical structure identification.Acta obstetricia et gynecologica Scandinavica · 2025Pooled it
- Untangling surgical gesture analysis-are we even speaking the same language? a systematic review.Surgical endoscopy · 2025Pooled it
- Artificial intelligence for X-ray scaphoid fracture detection: a systematic review and diagnostic test accuracy meta-analysis.European radiology · 2024Pooled it
- Deep learning in voice analysis for diagnosing vocal cord pathologies: a systematic review.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Pooled it
- Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review.Surgical endoscopy · 2023Pooled it
- A systematic review of annotation for surgical process model analysis in minimally invasive surgery based on video.Surgical endoscopy · 2023Pooled it
- Computer-aided anatomy recognition in intrathoracic and -abdominal surgery: a systematic review.Surgical endoscopy · 2022Pooled it
- An intraoperative artificial intelligence system identifying anatomical landmarks for laparoscopic cholecystectomy: a prospective clinical feasibility trial (J-SUMMIT-C-01).Surgical endoscopy · 2023Trial
- Artificial intelligence-based model for identifying anatomical and surgical structures in the videos of laparoscopic left hemicolectomy.Journal of robotic surgery · 2026Article
- Deep learning-based renal artery segmentation and angle estimation for registration of endoscopic images and 3D models in robot-assisted partial nephrectomy.International journal of computer assisted radiology and surgery · 2026Article
- The first reported case using real-time artificial intelligence with single-port robotics for colorectal surgery.Surgical endoscopy · 2026Article
- Automatic and Real-Time Surgeon's Gazing Point Detection From Surgical Videos Using Machine Learning and Mathematical Algorithm.Journal of hepato-biliary-pancreatic sciences · 2026Article
- Associations Between Eye-Movement Patterns, Pupil Dynamics, and the Interpretation of a Single Mixed-Dentition Panoramic Radiograph Among Dental Students: An Exploratory Eye-Tracking Study.Vision (Basel, Switzerland) · 2026Article
- Prediction of surgery start for automated anesthesia using draping detection from surveillance videos.Journal of clinical monitoring and computing · 2026Article
- Artificial intelligence in thoracic surgery: Perspectives and challenges.Surgery in practice and science · 2025Review
- A Novel Deep Learning Model for Predicting Colorectal Anastomotic Leakage: A Pioneer Multicenter Transatlantic Study.Journal of clinical medicine · 2025Article
- Deep learning-based intraoperative visual guidance model for ureter identification in laparoscopic sigmoidectomy.Surgical endoscopy · 2025Article
- Artificial intelligence classifies surgical technical skills in simulated laparoscopy: a pilot study.Surgical endoscopy · 2025Article
- A Comprehensive Video Dataset for Surgical Laparoscopic Action Analysis.Scientific data · 2025Article
- A pioneering artificial intelligence tool to predict treatment outcomes in ovarian cancer via diagnostic laparoscopy.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn the past decade, deep learning has revolutionized medical image processing. This technique may advance laparoscopic surgery. Study objective was to evaluate whether deep learning networks accurately analyze videos of laparoscopic procedures.
methodsMedline, Embase, IEEE Xplore, and the Web of science databases were searched from January 2012 to May 5, 2020. Selected studies tested a deep learning model, specifically convolutional neural networks, for video analysis of laparoscopic surgery. Study characteristics including the dataset source, type of operation, number of videos, and prediction application were compared. A random effects model was used for estimating pooled sensitivity and specificity of the computer algorithms. Summary receiver operating characteristic curves were calculated by the bivariate model of Reitsma.
resultsThirty-two out of 508 studies identified met inclusion criteria. Applications included instrument recognition and detection (45%), phase recognition (20%), anatomy recognition and detection (15%), action recognition (13%), surgery time prediction (5%), and gauze recognition (3%). The most common tested procedures were cholecystectomy (51%) and gynecological-mainly hysterectomy and myomectomy (26%). A total of 3004 videos were analyzed. Publications in clinical journals increased in 2020 compared to bio-computational ones. Four studies provided enough data to construct 8 contingency tables, enabling calculation of test accuracy with a pooled sensitivity of 0.93 (95% CI 0.85-0.97) and specificity of 0.96 (95% CI 0.84-0.99). Yet, the majority of papers had a high risk of bias.
conclusionsDeep learning research holds potential in laparoscopic surgery, but is limited in methodologies. Clinicians may advance AI in surgery, specifically by offering standardized visual databases and reporting.
Indexed as
Identifiers
33398560What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.