DAISI: Database for AI Surgical Instruction

• URL: http://arxiv.org/abs/2004.02809v1
• Date: Sun, 22 Mar 2020 22:07:43 GMT
• Title: DAISI: Database for AI Surgical Instruction
• Authors: Edgar Rojas-Mu\~noz, Kyle Couperus and Juan Wachs
• Abstract summary: Telementoring surgeons as they perform surgery can be essential in the treatment of patients when in situ expertise is not available. When mentors are unavailable, a fallback autonomous mechanism should provide medical practitioners with the required guidance. This work presents the first Database for AI Surgical Instruction (DAISI)
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Telementoring surgeons as they perform surgery can be essential in the treatment of patients when in situ expertise is not available. Nonetheless, expert mentors are often unavailable to provide trainees with real-time medical guidance. When mentors are unavailable, a fallback autonomous mechanism should provide medical practitioners with the required guidance. However, AI/autonomous mentoring in medicine has been limited by the availability of generalizable prediction models, and surgical procedures datasets to train those models with. This work presents the initial steps towards the development of an intelligent artificial system for autonomous medical mentoring. Specifically, we present the first Database for AI Surgical Instruction (DAISI). DAISI leverages on images and instructions to provide step-by-step demonstrations of how to perform procedures from various medical disciplines. The dataset was acquired from real surgical procedures and data from academic textbooks. We used DAISI to train an encoder-decoder neural network capable of predicting medical instructions given a current view of the surgery. Afterwards, the instructions predicted by the network were evaluated using cumulative BLEU scores and input from expert physicians. According to the BLEU scores, the predicted and ground truth instructions were as high as 67% similar. Additionally, expert physicians subjectively assessed the algorithm using Likert scale, and considered that the predicted descriptions were related to the images. This work provides a baseline for AI algorithms to assist in autonomous medical mentoring.

Related papers

• Toward a Surgeon-in-the-Loop Ophthalmic Robotic Apprentice using Reinforcement and Imitation Learning [18.72371138886818]
  We propose an image-guided approach for surgeon-centered autonomous agents during ophthalmic cataract surgery. By integrating the surgeon's actions and preferences into the training process, our approach enables the robot to implicitly learn and adapt to the individual surgeon's unique techniques.
  arXiv  Detail & Related papers  (2023-11-29T15:00:06Z)
• Auditing for Human Expertise [12.967730957018688]
  We develop a statistical framework under which we can pose this question as a natural hypothesis test. We propose a simple procedure which tests whether expert predictions are statistically independent from the outcomes of interest. A rejection of our test thus suggests that human experts may add value to any algorithm trained on the available data.
  arXiv  Detail & Related papers  (2023-06-02T16:15:24Z)
• Surgical tool classification and localization: results and methods from the MICCAI 2022 SurgToolLoc challenge [69.91670788430162]
  We present the results of the SurgLoc 2022 challenge. The goal was to leverage tool presence data as weak labels for machine learning models trained to detect tools. We conclude by discussing these results in the broader context of machine learning and surgical data science.
  arXiv  Detail & Related papers  (2023-05-11T21:44:39Z)
• Towards Medical Artificial General Intelligence via Knowledge-Enhanced Multimodal Pretraining [121.89793208683625]
  Medical artificial general intelligence (MAGI) enables one foundation model to solve different medical tasks. We propose a new paradigm called Medical-knedge-enhanced mulTimOdal pretRaining (MOTOR)
  arXiv  Detail & Related papers  (2023-04-26T01:26:19Z)
• Live image-based neurosurgical guidance and roadmap generation using unsupervised embedding [53.992124594124896]
  We present a method for live image-only guidance leveraging a large data set of annotated neurosurgical videos. A generated roadmap encodes the common anatomical paths taken in surgeries in the training set. We trained and evaluated the proposed method with a data set of 166 transsphenoidal adenomectomy procedures.
  arXiv  Detail & Related papers  (2023-03-31T12:52:24Z)
• 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)
• Before and After: Machine learning for perioperative patient care [0.0]
  Cross-disciplinary review aims to build a bridge between computer science and nursing. It outlines and classifies the methods for machine learning and data processing in patient care before and after the operation.
  arXiv  Detail & Related papers  (2022-01-20T09:55:12Z)
• Deep Neural Networks for the Assessment of Surgical Skills: A Systematic Review [6.815366422701539]
  We have reviewed 530 papers, of which we selected 25 for this systematic review. We concluded that Deep Neural Networks are powerful tools for automated, objective surgical skill assessment using both kinematic and video data. The field would benefit from large, publicly available, annotated datasets that are representative of the surgical trainee and expert demographics and multimodal data beyond kinematics and videos.
  arXiv  Detail & Related papers  (2021-03-03T10:08:37Z)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)
• Recurrent and Spiking Modeling of Sparse Surgical Kinematics [0.8458020117487898]
  A growing number of studies have used machine learning to analyze video and kinematic data captured from surgical robots. In this study, we explore the possibility of using only kinematic data to predict surgeons of similar skill levels. We report that it is possible to identify surgical fellows receiving near perfect scores in the simulation exercises based on their motion characteristics alone.
  arXiv  Detail & Related papers  (2020-05-12T15:41:45Z)
• Automatic Gesture Recognition in Robot-assisted Surgery with Reinforcement Learning and Tree Search [63.07088785532908]
  We propose a framework based on reinforcement learning and tree search for joint surgical gesture segmentation and classification. Our framework consistently outperforms the existing methods on the suturing task of JIGSAWS dataset in terms of accuracy, edit score and F1 score.
  arXiv  Detail & Related papers  (2020-02-20T13:12:38Z)

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.