Smart Operation Theatre: An AI-based System for Surgical Gauze Counting

• URL: http://arxiv.org/abs/2603.20752v1
• Date: Sat, 21 Mar 2026 10:49:37 GMT
• Title: Smart Operation Theatre: An AI-based System for Surgical Gauze Counting
• Abstract summary: During surgeries, there is a risk of medical gauzes being left inside patients' bodies, leading to "Gossypiboma"<n>In partnership with Singapore General Hospital we have developed an AI-based system for gauze counting in surgical settings.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: During surgeries, there is a risk of medical gauzes being left inside patients' bodies, leading to "Gossypiboma" in patients and can cause serious complications in patients and also lead to legal problems for hospitals from malpractice lawsuits and regulatory penalties. Diagnosis depends on imaging methods such as X-rays or CT scans, and the usual treatment involves surgical excision. Prevention methods, such as manual counts and RFID-integrated gauzes, aim to minimize gossypiboma risks. However, manual tallying of 100s of gauzes by nurses is time-consuming and diverts resources from patient care. In partnership with Singapore General Hospital (SGH) we have developed a new prevention method, an AI-based system for gauze counting in surgical settings. Utilizing real-time video surveillance and object recognition technology powered by YOLOv5, a Deep Learning model was designed to monitor gauzes on two designated trays labelled "In" and "Out". Gauzes are tracked from the "In" tray, prior to their use in the patient's body & in the "Out" tray post-use, ensuring accurate counting and verifying that no gauze remains inside the patient at the end of the surgery. We have trained it using numerous images from Operation Theatres & augmented it to satisfy all possible scenarios. This study has also addressed the shortcomings of previous project iterations. Previously, the project employed two models: one for human detection and another for gauze detection, trained on a total of 2800 images. Now we have an integrated model capable of identifying both humans and gauzes, using a training set of 11,000 images. This has led to improvements in accuracy and increased the frame rate from 8 FPS to 15 FPS now. Incorporating doctor's feedback, the system now also supports manual count adjustments, enhancing its reliability in actual surgeries.

Related papers

• Modular Robotic Catheters for Endovascular Aneurysm Repair [55.52876285801259]
  endovascular aneurysm repair require surgeons to navigate catheters and guidewires into various branches of the abdominal aorta.<n>Previous clinical studies suggest that surgeons continue to struggle with vessel access using standard commercial instruments.<n>We present two contributions to solving this problem: 1) A bespoke 2-segment steerable catheter, consisting of 4 degrees of freedom to enhance dexterity.
  arXiv  Detail & Related papers  (2026-07-28T14:53:03Z)
• YOLOv10-Based Multi-Task Framework for Hand Localization and Laterality Classification in Surgical Videos [5.504955093712013]
  We propose a framework that simultaneously localizes hands and classifies their laterality in complex surgical scenes.<n>The model is trained on the Trauma THOMPSON Challenge 2025 Task 2 dataset.
  arXiv  Detail & Related papers  (2026-02-21T21:41:56Z)
• Semantic Segmentation for Preoperative Planning in Transcatheter Aortic Valve Replacement [61.573750959726475]
  We consider medical guidelines for preoperative planning of the transcatheter aortic valve replacement (TAVR) and identify tasks that may be supported via semantic segmentation models.<n>We first derive fine-grained TAVR-relevant pseudo-labels from coarse-grained anatomical information, in order to train segmentation models and quantify how well they are able to find these structures in the scans.
  arXiv  Detail & Related papers  (2025-07-22T13:24:45Z)
• Monocular Marker-free Patient-to-Image Intraoperative Registration for Cochlear Implant Surgery [4.250558597144547]
  Our framework seamlessly integrates with monocular surgical microscopes, making it highly practical for clinical use without additional hardware dependencies and requirements.<n>Our results suggest that our approach achieves clinically relevant accuracy in predicting 6D camera poses for registering 3D preoperative CT scans to 2D surgical scenes with an angular error within 10 degrees in most cases.
  arXiv  Detail & Related papers  (2025-05-23T21:15:00Z)
• Automated Surgical Skill Assessment in Endoscopic Pituitary Surgery using Real-time Instrument Tracking on a High-fidelity Bench-top Phantom [9.41936397281689]
  Improved surgical skill is generally associated with improved patient outcomes, but assessment is subjective and labour-intensive. A new public dataset is introduced, focusing on simulated surgery, using the nasal phase of endoscopic pituitary surgery as an exemplar. A Multilayer Perceptron achieved 87% accuracy in predicting surgical skill level (novice or expert), with the "ratio of total procedure time to instrument visible time" correlated with higher surgical skill.
  arXiv  Detail & Related papers  (2024-09-25T15:27:44Z)
• EndoGSLAM: Real-Time Dense Reconstruction and Tracking in Endoscopic Surgeries using Gaussian Splatting [53.38166294158047]
  EndoGSLAM is an efficient approach for endoscopic surgeries, which integrates streamlined representation and differentiable Gaussianization. Experiments show that EndoGSLAM achieves a better trade-off between intraoperative availability and reconstruction quality than traditional or neural SLAM approaches.
  arXiv  Detail & Related papers  (2024-03-22T11:27:43Z)
• Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgery [52.63271687382495]
  We present an approach that automatically solves the registration problem for lumbar spinal fusion surgery in a radiation-free manner. A deep neural network was trained to segment the lumbar spine and simultaneously predict its orientation, yielding an initial pose for preoperative models. An intuitive surgical guidance is provided thanks to the integration into an augmented reality based navigation system.
  arXiv  Detail & Related papers  (2023-08-05T16:26:41Z)
• GLSFormer : Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos [57.93194315839009]
  We propose a vision transformer-based approach to learn temporal features directly from sequence-level patches. We extensively evaluate our approach on two cataract surgery video datasets, Cataract-101 and D99, and demonstrate superior performance compared to various state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-07-20T17:57:04Z)
• Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification [42.75911994044675]
  We present a novel approach for unpaired image-to-image translation of prostate MRIs and an uncertainty-aware training approach for classifying clinically significant PCa. Our approach involves a novel pipeline for translating unpaired 3.0T multi-parametric prostate MRIs to 1.5T, thereby augmenting the available training data. Our experiments demonstrate that the proposed method significantly improves the Area Under ROC Curve (AUC) by over 20% compared to the previous work.
  arXiv  Detail & Related papers  (2023-07-02T05:26:54Z)
• Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing [88.99939660183881]
  We propose a framework for autonomous robotic navigation for subretinal injection. Our method consists of an instrument pose estimation method, an online registration between the robotic and the i OCT system, and trajectory planning tailored for navigation to an injection target. Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method.
  arXiv  Detail & Related papers  (2023-01-17T21:41:21Z)
• The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: Challenges and methods [15.833413083110903]
  This paper presents ESAD, the first large-scale dataset designed to tackle the problem of surgeon action detection in endoscopic minimally invasive surgery. The dataset provides bounding box annotation for 21 action classes on real endoscopic video frames captured during prostatectomy, and was used as the basis of a recent MIDL 2020 challenge.
  arXiv  Detail & Related papers  (2021-04-07T15:11:51Z)
• Single-Shot Pose Estimation of Surgical Robot Instruments' Shafts from Monocular Endoscopic Images [8.966454850713522]
  We propose a new method to estimate the pose of the surgical instruments' shafts using a monocular endoscope. Our method is based on the use of an automatically annotated training dataset and an improved pose-estimation deep-learning architecture.
  arXiv  Detail & Related papers  (2020-03-03T00:38:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.