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An original deep learning model using limited data for COVID-19 discrimination: A multicenter study.

Fangyi Xu1, Kaihua Lou1, Chao Chen1

  • 1Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Medical Physics
|March 19, 2022
PubMed
Summary

A novel artificial intelligence (AI) algorithm, 3DMTM, demonstrates robust COVID-19 diagnosis using limited chest CT data. This AI tool shows comparable performance to multi-modal data, enhancing clinical application potential.

Keywords:
artificial intelligencecoronavirus disease 2019deep learningspiral computedtomography

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Area of Science:

  • Medical Imaging and Artificial Intelligence
  • Computational Pathology
  • Machine Learning in Healthcare

Background:

  • Artificial intelligence (AI) shows promise for COVID-19 diagnosis but faces challenges with large data requirements and limited generalizability.
  • Clinical application of AI for COVID-19 detection is hindered by the need for extensive datasets and robust algorithm performance.

Purpose of the Study:

  • To develop a highly robust AI algorithm for COVID-19 discrimination using limited chest CT data.
  • To evaluate the generalizability and performance of the developed AI algorithm against existing methods.

Main Methods:

  • A novel three-dimensional algorithm combining multi-instance learning with LSTM (3DMTM) was developed.
  • The 3DMTM algorithm was trained and validated on datasets of varying sizes, including limited data scenarios, and compared against logistic regression, k-nearest neighbor, support vector machine, and a 3D convolutional neural network.
  • Performance was evaluated using metrics such as AUC, sensitivity, specificity, accuracy, and F1 score on an independent external test set.

Main Results:

  • The 3DMTM algorithm achieved an AUC of 0.956 on the external test cohort using a large dataset and 0.937 using a limited dataset.
  • A 3D convolutional neural network's performance decreased significantly with reduced training data, highlighting 3DMTM's robustness.
  • While clinical information improved other models, 3DMTM demonstrated excellent performance using CT data alone, comparable to multi-modal data.

Conclusions:

  • The 3DMTM algorithm exhibits excellent robustness for COVID-19 discrimination, even with limited chest CT data.
  • 3DMTM's performance using CT data is comparable to models trained with multi-modal information, suggesting its potential for streamlined clinical integration.
  • The study underscores the potential of AI, specifically 3DMTM, to overcome data limitations in diagnosing COVID-19 effectively.