MyoVision-US:人工智能驱动的骨骼肌超声自动分析软件

Zoe Calulo Rivera, Felipe González-Seguel, Arimitsu Horikawa-Strakovsky, Catherine Granger, Aarti Sarwal, Sanjay Dhar, George Ntoumenopoulos, Jin Chen, V. K. Cody Bumgardner, Selina M. Parry, Kirby P. Mayer, Yuan Wen
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引用次数: 0

摘要

导言/目的 肌肉超声在临床实践和研究中具有很高的实用性;然而,要实现形态学的客观量化,主要挑战在于人工分析所需的培训和时间。本研究旨在开发和验证一种由人工智能(AI)驱动的软件工具,通过测量其对健康、急性和慢性疾病受试者下肢肌肉超声图像进行量化的专家人工分析的一致性和可预测性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MyoVision-US: an Artificial Intelligence-Powered Software for Automated Analysis of Skeletal Muscle Ultrasonography
Introduction/Aims Muscle ultrasound has high utility in clinical practice and research; however, the main challenges are the training and time required for manual analysis to achieve objective quantification of morphometry. This study aimed to develop and validate a software tool powered by artificial intelligence (AI) by measuring its consistency and predictability of expert manual analysis quantifying lower limb muscle ultrasound images across healthy, acute, and chronic illness subjects.
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