Scholarlink

Introduction <p>In the Movement Disorder Society criteria for the diagnosis of Parkinson’s disease (PD), evaluation of the presynaptic dopamine system should be performed using dopamine transporter single-photon emission computed tomography (DAT SPECT). However, it is difficult for unexperienced physicians to detect a mild defect. Here, we attempted to develop a simple deep learning-based image analysis method to evaluate DAT SPECT images.</p> Methods <p>We used data from 300 patients who were diagnosed with PD and 102 control patients with non-neurodegenerative diseases as the artificial intelligence (AI) development cohort. For validation, we analyzed the data of 96 patients with PD from an independent cohort. We divided the development cohort into the training and test sets. Using the training set, we performed transfer learning using six pre-trained convolutional neural network architectures, and created AI models. We evaluated their accuracy, sensitivity, and area under the receiver operating characteristic curve, and further confirmed their validity by using the validation cohort. In addition, we compared the accuracy of the best AI model with that of two experienced neurologists and a resident.</p> Results <p>The selected AI model could interpret DAT SPECT images with an accuracy of 0.959; accuracy in the validation cohort was 0.8959–1. There was no significant difference between the accuracy of the AI model and physicians.</p> Conclusion <p>Our simple AI model for the interpretation of DAT SPECT images was accurate and robust. Its accuracy was equivalent to that of physicians.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Examination of simple artificial intelligence-based analysis of dopamine transporter scintigraphy for supporting a diagnosis of Parkinson’s disease

  • 期刊论文

  • 发表日期：

    2025年11月26日

查看全文
  • Atsunori Murao,
  • Kazuhiro Hara,
  • Shintaro Oyama,
  • Aya Ogura,
  • Yoshiyuki Kishimoto,
  • Mai Hatanaka,
  • Naotoshi Fujita,
  • Misaki Sato,
  • Ikuko Aiba,
  • Katsuhiko Kato,
  • Masahisa Katsuno

摘要

Introduction

In the Movement Disorder Society criteria for the diagnosis of Parkinson’s disease (PD), evaluation of the presynaptic dopamine system should be performed using dopamine transporter single-photon emission computed tomography (DAT SPECT). However, it is difficult for unexperienced physicians to detect a mild defect. Here, we attempted to develop a simple deep learning-based image analysis method to evaluate DAT SPECT images.

Methods

We used data from 300 patients who were diagnosed with PD and 102 control patients with non-neurodegenerative diseases as the artificial intelligence (AI) development cohort. For validation, we analyzed the data of 96 patients with PD from an independent cohort. We divided the development cohort into the training and test sets. Using the training set, we performed transfer learning using six pre-trained convolutional neural network architectures, and created AI models. We evaluated their accuracy, sensitivity, and area under the receiver operating characteristic curve, and further confirmed their validity by using the validation cohort. In addition, we compared the accuracy of the best AI model with that of two experienced neurologists and a resident.

Results

The selected AI model could interpret DAT SPECT images with an accuracy of 0.959; accuracy in the validation cohort was 0.8959–1. There was no significant difference between the accuracy of the AI model and physicians.

Conclusion

Our simple AI model for the interpretation of DAT SPECT images was accurate and robust. Its accuracy was equivalent to that of physicians.