ArticleSurgical endoscopy2024
Deep learning-based automatic bleeding recognition during liver resection in laparoscopic hepatectomy.
Article in Surgical endoscopy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis 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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-based automated bleeding monitoring in conventional and robot-assisted laparoscopic surgery: a systematic review.Journal of robotic surgery · 2026Pooled it
- Laparoscopic image segmentation of the hepatobiliary system: Current status and advances from a deep learning perspective.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIntraoperative hemorrhage during laparoscopic hepatectomy (LH) is a risk factor for negative postoperative outcomes. Ensuring appropriate hemostasis enhances the safety of surgical procedures. An automatic bleeding recognition system based on deep learning can lead to safer surgeries; however, deep learning models that are useful for detecting and stopping bleeding in LH have not yet been reported. In this study, we aimed to develop a deep learning model to automatically recognize bleeding regions during liver transection in LH.
methodsIn this retrospective feasibility study, bleeding scenes were randomly selected from LH videos, and the videos were divided into frames at 30 frames per second. Bleeding regions within the images were annotated by pixels, and subsequently, all images were assigned to the training, validation, and test datasets to develop the deep learning model. A convolutional neural network algorithm was used to perform semantic segmentation. After training and validation, the model was evaluated using images from the test dataset. Precision, recall, and Dice coefficients served as the evaluation metrics for the model.
resultsIn total, 2203 annotated images from 44 LH videos were utilized and divided into 1500, 400, and 303 frames for the training, validation, and test datasets, respectively. The precision, recall, and Dice coefficient values of the model were 0.76, 0.79, and 0.77, respectively.
conclusionsWe developed an automatic bleeding recognition model based on semantic segmentation and verified its performance. The proposed model is potentially useful for intraoperative alerting or evaluating surgical skills in the future.
Indexed as
Identifiers
39557646What 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.