ArticleInternational journal of computer assisted radiology and surgery2021
Object and anatomical feature recognition in surgical video images based on a convolutional neural network.
Article in International journal of computer assisted radiology and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computer-aided anatomy recognition in intrathoracic and -abdominal surgery: a systematic review.Surgical endoscopy · 2022Pooled it
- Detection and tracking of a gauze sponge in minimally invasive surgery using a YOLO and R-CNN based model.Medical & biological engineering & computing · 2026Article
- Bibliometric analysis of deep learning for surgical instrument segmentation, detection and tracking in minimally invasive surgery.Frontiers in digital health · 2026Review
- Surgical education reimagined: the convergence of learning theories and artificial intelligence.BMC medical education · 2025Review
- A Standardized Temporal Segmentation Framework and Annotation Resource Library in Robotic Surgery.Mayo Clinic proceedings. Digital health · 2025Article
- Intraoperative navigation system for liver resection based on edge-AI and multimodal AI.Surgical endoscopy · 2025Article
- Use of artificial intelligence in the analysis of digital videos of invasive surgical procedures: scoping review.BJS open · 2025Article
- Automated assessment of simulated laparoscopic surgical skill performance using deep learning.Scientific reports · 2025Article
- Evolution of Artificial Intelligence in Medical Education From 2000 to 2024: Bibliometric Analysis.Interactive journal of medical research · 2025Article
- Vision techniques for anatomical structures in laparoscopic surgery: a comprehensive review.Frontiers in surgery · 2025Review
- Deep learning-based surgical phase recognition in laparoscopic cholecystectomy.Annals of hepato-biliary-pancreatic surgery · 2024Article
- Intraoperative detection of parathyroid glands using artificial intelligence: optimizing medical image training with data augmentation methods.Surgical endoscopy · 2024Article
- An Automated Video Analysis System for Retrospective Assessment and Real-Time Monitoring of Endoscopic Procedures (with Video).Bioengineering (Basel, Switzerland) · 2024Article
- An artificial intelligence model for the radiographic diagnosis of osteoarthritis of the temporomandibular joint.Scientific reports · 2023Article
- Evolution of the digital operating room: the place of video technology in surgery.Langenbeck's archives of surgery · 2023Review
- Computer-vision based analysis of the neurosurgical scene - A systematic review.Brain & spine · 2023Review
- Digital workflows for pathological assessment of rat estrous cycle stage using images of uterine horn and vaginal tissue.Journal of pathology informatics · 2022Article
- Deep-Learning-Based Cerebral Artery Semantic Segmentation in Neurosurgical Operating Microscope Vision Using Indocyanine Green Fluorescence Videoangiography.Frontiers in neurorobotics · 2021Article
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7 authors.
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Abstract
purposeArtificial intelligence-enabled techniques can process large amounts of surgical data and may be utilized for clinical decision support to recognize or forecast adverse events in an actual intraoperative scenario. To develop an image-guided navigation technology that will help in surgical education, we explored the performance of a convolutional neural network (CNN)-based computer vision system in detecting intraoperative objects.
methodsThe surgical videos used for annotation were recorded during surgeries conducted in the Department of Surgery of Tokyo Women's Medical University from 2019 to 2020. Abdominal endoscopic images were cut out from manually captured surgical videos. An open-source programming framework for CNN was used to design a model that could recognize and segment objects in real time through IBM Visual Insights. The model was used to detect the GI tract, blood, vessels, uterus, forceps, ports, gauze and clips in the surgical images.
resultsThe accuracy, precision and recall of the model were 83%, 80% and 92%, respectively. The mean average precision (mAP), the calculated mean of the precision for each object, was 91%. Among surgical tools, the highest recall and precision of 96.3% and 97.9%, respectively, were achieved for forceps. Among the anatomical structures, the highest recall and precision of 92.9% and 91.3%, respectively, were achieved for the GI tract.
conclusionThe proposed model could detect objects in operative images with high accuracy, highlighting the possibility of using AI-based object recognition techniques for intraoperative navigation. Real-time object recognition will play a major role in navigation surgery and surgical education.
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