Development of Artificial Intelligence to Support Needle Electromyography Diagnostic Analysis

남상우; 손민균; 김현아; 공현중; 정일영

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자료유형: 학술저널

저자정보: 남상우 손민균 김현아 공현중 정일영

저널정보: 대한의료정보학회 Healthcare Informatics Research Healthcare Informatics Research 제25권 제2호

발행연도: 2019.1

수록면: 131 - 138 (8page)

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Objectives: This study proposes a method for classifying three types of resting membrane potential signals obtained as images through diagnostic needle electromyography (EMG) using TensorFlow-Slim and Python to implement an artificial-intelligence- based image recognition scheme. Methods: Waveform images of an abnormal resting membrane potential generated by diagnostic needle EMG were classified into three types—positive sharp waves (PSW), fibrillations (Fibs), and Others— using the TensorFlow-Slim image classification model library. A total of 4,015 raw waveform data instances were reviewed, with 8,576 waveform images subsequently collected for training. Images were learned repeatedly through a convolutional neural network. Each selected waveform image was classified into one of the aforementioned categories according to the learned results. Results: The classification model, Inception v4, was used to divide waveform images into three categories (accuracy = 93.8%, precision = 99.5%, recall = 90.8%). This was done by applying the pretrained Inception v4 model to a fine-tuning method. The image recognition model was created for training using various types of image-based medical data. Conclusions: The TensorFlow-Slim library can be used to train and recognize image data, such as EMG waveforms, through simple coding rather than by applying TensorFlow. It is expected that a convolutional neural network can be applied to image data such as the waveforms of electrophysiological signals in a body based on this study.

참고문헌 (11)

참고문헌 신청

Lindstrom H / 2018 / The usefulness of electrodiagnostic studies in the diagnosis and management of neuromuscular disorders / Muscle Nerve 58(2):191~196

Nariman Noorbakhsh-Sabet / 2019 / Artificial Intelligence Transforms the Future of Health Care / The American Journal of Medicine 132(7):795~801

NVIDIA CUDA Toolkit / NVIDIA

TensorFlow.org / GPU support / TensorFlow.org

NVIDIA cuDNN

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