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Towards precision medicine based on a continuous deep learning optimization and ensemble approach.

Jian Li1, Linyuan Jin2, Zhiyuan Wang3

  • 1Department of Ultrasound, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China. 707779628@qq.com.

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Summary
This summary is machine-generated.

A new continuous learning system (CLS) using deep learning accurately diagnoses breast masses, outperforming most physicians. This AI-driven approach shows promise for precision medicine and broader applications in medical diagnostics.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis
  • Oncology Diagnostics

Background:

  • Accurate diagnosis of breast masses is crucial for effective treatment and patient outcomes.
  • Current diagnostic methods can be subjective and vary in accuracy.
  • The integration of artificial intelligence (AI) offers potential for enhanced diagnostic precision.

Purpose of the Study:

  • To develop and evaluate a continuous learning system (CLS) for precise diagnosis of breast masses using ultrasound images.
  • To assess the diagnostic performance of the CLS in classifying tumors, pathological types, and diseases.
  • To compare the diagnostic capabilities of the CLS against human physicians.

Main Methods:

  • Development of a CLS integrating deep learning, optimization, and ensemble techniques.
  • Training the model on a dataset of 629 breast masses and 2235 ultrasound images from 561 cases.
  • Validation using seven independent datasets from external institutions and comparison with 21 physicians.

Main Results:

  • The CLS demonstrated diagnostic performance that surpassed that of 20 out of 21 physicians by the sixth training stage.
  • The system achieved high accuracy in diagnosing benign and malignant tumors, pathological types, and diseases.
  • The developed integrated method showed strong potential for accurate breast mass diagnosis.

Conclusions:

  • The developed CLS shows significant potential for accurate and reliable diagnosis of breast masses.
  • This AI-driven system can aid clinicians in precision medicine, improving diagnostic workflows.
  • The methodology is adaptable for intelligent diagnosis of masses in other organs, highlighting broad applicability.