Nahid: AI-based Algorithm for operating fully-automatic surgery

• URL: http://arxiv.org/abs/2401.08584v1
• Date: Fri, 3 Nov 2023 12:35:07 GMT
• Title: Nahid: AI-based Algorithm for operating fully-automatic surgery
• Abstract summary: In this paper, for the first time, a method is presented that can provide a fully automated surgery based on software and computer vision techniques. A more detailed algorithm is presented that is capable of automatically diagnosing and treating this disease during surgery.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, for the first time, a method is presented that can provide a fully automated surgery based on software and computer vision techniques. Then, the advantages and challenges of computerization of medical surgery are examined. Finally, the surgery related to isolated ovarian endometriosis disease has been examined, and based on the presented method, a more detailed algorithm is presented that is capable of automatically diagnosing and treating this disease during surgery as proof of our proposed method where a U-net is trained to detect the endometriosis during surgery.

Related papers

• Predicting the risk of colorectal anastomotic leak based on preoperative mapping of the blood supply of the bowel [0.5833117322405447]
  Anastomotic leak remains one of the most serious complications following colorectal cancer surgery.<n>No validated CT-based method exists to predict anastomotic leak risk prior to surgery.
  arXiv  Detail & Related papers  (2026-06-01T12:20:43Z)
• From Preoperative CT to Postmastoidectomy Mesh Construction: Mastoidectomy Shape Prediction for Cochlear Implant Surgery [3.2395357384758996]
  We propose a hybrid self-supervised and weakly-supervised learning framework to predict the mastoidectomy region directly from preoperative CT scans.<n>Our method achieves a mean Dice score of 0.72 when predicting the complex and boundary-less mastoidectomy shape.
  arXiv  Detail & Related papers  (2026-01-07T21:23:35Z)
• GLENDA: Gynecologic Laparoscopy Endometriosis Dataset [4.657080645343133]
  Gynecologic Laparoscopy ENdometriosis DAtaset is an image dataset containing region-based annotations of a common medical condition named endometriosis.<n>The dataset is the first of its kind and it has been created in collaboration with leading medical experts in the field.
  arXiv  Detail & Related papers  (2025-08-29T08:15:27Z)
• Benchmarking Laparoscopic Surgical Image Restoration and Beyond [54.28852320829451]
  In laparoscopic surgery, a clear and high-quality visual field is critical for surgeons to make accurate decisions.<n> persistent visual degradation, including smoke generated by energy devices, lens fogging from thermal gradients, and lens contamination pose risks to patient safety.<n>We introduce a real-world open-source surgical image restoration dataset covering laparoscopic environments, called SurgClean.
  arXiv  Detail & Related papers  (2025-05-25T14:17:56Z)
• Cataract-1K: Cataract Surgery Dataset for Scene Segmentation, Phase Recognition, and Irregularity Detection [5.47960852753243]
  We present the largest cataract surgery video dataset that addresses diverse requisites for constructing computerized surgical workflow analysis. We validate the quality of annotations by benchmarking the performance of several state-of-the-art neural network architectures. The dataset and annotations will be publicly available upon acceptance of the paper.
  arXiv  Detail & Related papers  (2023-12-11T10:53:05Z)
• Redefining the Laparoscopic Spatial Sense: AI-based Intra- and Postoperative Measurement from Stereoimages [3.2039076408339353]
  We develop a novel human-AI-based method for laparoscopic measurements utilizing stereo vision. Based on a holistic qualitative requirements analysis, this work proposes a comprehensive measurement method. Our results outline the potential of our method achieving high accuracies in distance measurements with errors below 1 mm.
  arXiv  Detail & Related papers  (2023-11-16T10:19:04Z)
• 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)
• Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges [58.32937972322058]
  "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic.
  arXiv  Detail & Related papers  (2023-07-30T16:08:45Z)
• CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection [41.66666272822756]
  This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection. It includes weakly-supervised bounding box localization of every visible surgical instrument (or tool) as the key actors, and the modeling of each tool-activity in the form of instrument, verb, target> triplet.
  arXiv  Detail & Related papers  (2023-02-13T11:53:14Z)
• 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)
• Surgical Phase Recognition in Laparoscopic Cholecystectomy [57.929132269036245]
  We propose a Transformer-based method that utilizes calibrated confidence scores for a 2-stage inference pipeline. Our method outperforms the baseline model on the Cholec80 dataset, and can be applied to a variety of action segmentation methods.
  arXiv  Detail & Related papers  (2022-06-14T22:55:31Z)
• CholecTriplet2021: A benchmark challenge for surgical action triplet recognition [66.51610049869393]
  This paper presents CholecTriplet 2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. We present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods and 19 new deep learning algorithms are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%.
  arXiv  Detail & Related papers  (2022-04-10T18:51:55Z)
• Real-time Informative Surgical Skill Assessment with Gaussian Process Learning [12.019641896240245]
  This work presents a novel Gaussian Process Learning-based automatic objective surgical skill assessment method for ESSBSs. The proposed method projects the instrument movements into the endoscope coordinate to reduce the data dimensionality. The experimental results show that the proposed method reaches 100% prediction precision for complete surgical procedures and 90% precision for real-time prediction assessment.
  arXiv  Detail & Related papers  (2021-12-05T15:35:40Z)
• AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo [55.04215695343928]
  The aortic vessel tree is composed of the aorta and its branching arteries. We systematically review computing techniques for the automatic and semi-automatic segmentation of the aortic vessel tree.
  arXiv  Detail & Related papers  (2021-08-06T08:18:28Z)
• TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks [43.95869213955351]
  We propose a Multi-Stage Temporal Convolutional Network (MS-TCN) that performs hierarchical prediction refinement for surgical phase recognition. Our method is thoroughly evaluated on two datasets of laparoscopic cholecystectomy videos with and without the use of additional surgical tool information.
  arXiv  Detail & Related papers  (2020-03-24T10:12:30Z)

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.