ArticleScientific reports2021
Automated segmentation by deep learning of loose connective tissue fibers to define safe dissection planes in robot-assisted gastrectomy.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 2 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
52 citing papers in PubMed, 2 syntheses or guidelines pooled it, 73 citations in OpenAlex.
- Applications of artificial intelligence in robot-assisted surgery: a systematic review.Journal of robotic surgery · 2026Pooled it
- Computer-aided anatomy recognition in intrathoracic and -abdominal surgery: a systematic review.Surgical endoscopy · 2022Pooled it
- Artificial intelligence-based anatomical segmentation in transabdominal preperitoneal repair of groin hernia.Hernia : the journal of hernias and abdominal wall surgery · 2026Article
- AI-based assessment of surgical efficiency and exposure quality in laparoscopic distal gastrectomy using a phase recognition model.Surgical endoscopy · 2026Article
- Histopathological proof-of-concept validation of AI-assisted peripheral neural track recognition during laparoscopic gastrointestinal surgery.Surgical endoscopy · 2026Article
- AI-Based Automated Instrument Tracking During Endoscopic Skull Base Surgery: A Pilot Feasibility Study.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- Impact of artificial intelligence intervention on awareness of anatomical landmarks in laparoscopic gastrectomy: An exploratory study.Surgical endoscopy · 2026Article
- Intraoperative real-time recognition of dissectible layers by artificial intelligence is useful in laparoscopic inguinal hernia repair.Surgical endoscopy · 2026Article
- A proof-of-concept study of surgical-VLM for surgical support in robotic surgery: contextual benchmarking against ChatGPT-5.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026Article
- Reply to: Surgical scene understanding and the emerging challenge of independent validation in an industry-led AI ecosystem.NPJ digital medicine · 2026Article
- Deep learning for intraoperative recognition of critical structures in total hysterectomy.Surgical endoscopy · 2026Article
- Robotic adhesiolysis: current evidence, technical considerations, and future directions.Journal of robotic surgery · 2026Review
- Artificial Intelligence-Based Surgical Phase Analysis Enables Objective Assessment of Surgeon Skill in Robotic Distal Gastrectomy: A Multicenter Study.Annals of gastroenterological surgery · 2026Article
- The first reported case using real-time artificial intelligence with single-port robotics for colorectal surgery.Surgical endoscopy · 2026Article
- Human-in-the-loop validation of an artificial intelligence-driven real-time prostatic capsule recognition model for nerve-sparing robot-assisted radical prostatectomy.Surgical endoscopy · 2026Article
- Artificial Intelligence Applications in Gastric Cancer Surgery: Bridging Early Diagnosis and Responsible Precision Medicine.Journal of clinical medicine · 2026Review
- Deep learning approach for critical exposure during division of the inferior mesenteric artery in colorectal surgery.Journal of robotic surgery · 2026Article
- Deep learning-assisted near-real-time identification of the inferior mesenteric artery and vein during laparoscopic rectal resection: a single-center proof-of-concept study.Frontiers in medicine · 2026Article
- Artificial intelligence for surgical scene understanding: a systematic review and reporting quality meta-analysis.NPJ digital medicine · 2025Article
- Assessment of AI-driven anatomical recognition in robotic gastrectomy: A multicenter retrospective analysis.Surgical endoscopy · 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
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prediction of anatomical structures within the surgical field by artificial intelligence (AI) is expected to support surgeons' experience and cognitive skills. We aimed to develop a deep-learning model to automatically segment loose connective tissue fibers (LCTFs) that define a safe dissection plane. The annotation was performed on video frames capturing a robot-assisted gastrectomy performed by trained surgeons. A deep-learning model based on U-net was developed to output segmentation results. Twenty randomly sampled frames were provided to evaluate model performance by comparing Recall and F1/Dice scores with a ground truth and with a two-item questionnaire on sensitivity and misrecognition that was completed by 20 surgeons. The model produced high Recall scores (mean 0.606, maximum 0.861). Mean F1/Dice scores reached 0.549 (range 0.335-0.691), showing acceptable spatial overlap of the objects. Surgeon evaluators gave a mean sensitivity score of 3.52 (with 88.0% assigning the highest score of 4; range 2.45-3.95). The mean misrecognition score was a low 0.14 (range 0-0.7), indicating very few acknowledged over-detection failures. Thus, AI can be trained to predict fine, difficult-to-discern anatomical structures at a level convincing to expert surgeons. This technology may help reduce adverse events by determining safe dissection planes.
Indexed as
Identifiers
What 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.