Robotics & Machine Learning Daily News2024,Issue(Jun.19) :76-76.

University of Auckland Reports Findings in Brain Injury (Classification of short and long term mild traumatic brain injury using computerized eye tracking)

奥克兰大学报告脑损伤的发现(用计算机眼跟踪对短期和长期轻度脑损伤进行分类)

Robotics & Machine Learning Daily News2024,Issue(Jun.19) :76-76.

University of Auckland Reports Findings in Brain Injury (Classification of short and long term mild traumatic brain injury using computerized eye tracking)

奥克兰大学报告脑损伤的发现(用计算机眼跟踪对短期和长期轻度脑损伤进行分类)

扫码查看

摘要

一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-中枢神经系统疾病和状况的新研究-脑损伤是一篇报道的主题。根据NewsRx记者从新西兰奥克兰发回的新闻报道,Research称:“准确和客观的脑损伤诊断仍然是一个挑战。本研究评估了计算机眼追踪R评估(CEAs)在近期轻度创伤性脑损伤(mTBI)、持续性脑震荡后综合征(PPCS)和对照组中用于评估动眼功能、视觉注意/处理和选择性注意的可用性和可靠性。”新闻记者从Auc Kland大学的研究中获得了一句话,“测试包括自我中心定位、固定稳定性、平滑性、扫视、Stroop和前庭-眼反射(VOR)。35名健康成年人进行了两次CEA电池测试,以评估其可用性和测试-重测相关性。在单独的实验中,55名健康人、20名mTBI患者的CEA数据,以及20名mTBI患者的CEA数据使用40名PC成人训练机器学习模型,将参与者分为对照组、mTBI组或PCS组。组内相关系数显示中度(ICC>. 50)至优秀(ICC>. 98)可靠性(P<. 05)和令人满意的CEA依从性。机器学习模型将参与者分为控制组、mTBI组和PCS组,表现合理(平衡准确度控制:0.83,mAUC-ROC:0.82)。关键结果是VOR(凝视稳定性)、固定(垂直误差)和追踪(总误差、垂直增益和S加扫次数)。CEA电池可靠,能够相当好地区分健康、mTBI和PPCS患者。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Central Nervous System Diseases and Conditions - Brain Injury is the subject of a report. According to news reporting originating in Auckland, New Zealand, by NewsRx journalists, res earch stated, "Accurate, and objective diagnosis of brain injury remains challen ging. This study evaluated useability and reliability of computerized eye-tracke r assessments (CEAs) designed to assess oculomotor function, visual attention/pr ocessing, and selective attention in recent mild traumatic brain injury (mTBI), persistent post-concussion syndrome (PPCS), and controls." The news reporters obtained a quote from the research from the University of Auc kland, "Tests included egocentric localisation, fixation-stability, smooth-pursu it, saccades, Stroop, and the vestibulo-ocular reflex (VOR). Thirty-five healthy adults performed the CEA battery twice to assess useability and test-retest rel iability. In separate experiments, CEA data from 55 healthy, 20 mTBI, and 40 PPC S adults were used to train a machine learning model to categorize participants into control, mTBI, or PPCS classes. Intraclass correlation coefficients demonst rated moderate (ICC > .50) to excellent (ICC > .98) reliability (p <.05) and satisfactory CEA compliance . Machine learning modelling categorizing participants into groups of control, m TBI, and PPCS performed reasonably (balanced accuracy control: 0.83, mTBI: 0.66, and PPCS: 0.76, AUC-ROC: 0.82). Key outcomes were the VOR (gaze stability), fix ation (vertical error), and pursuit (total error, vertical gain, and number of s accades). The CEA battery was reliable and able to differentiate healthy, mTBI, and PPCS patients reasonably well."

Key words

Auckland/New Zealand/Australia and New Zealand/Brain Diseases and Conditions/Brain Injury/Central Nervous System Di seases and Conditions/Craniocerebral Trauma/Cyborgs/Emerging Technologies/He alth and Medicine/Machine Learning

引用本文复制引用

出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
段落导航相关论文