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Deep learning-based automated tool for diagnosing diabetic peripheral neuropathy.

Qincheng Qiao1,2, Juan Cao1,3,4,5, Wen Xue1,2

  • 1Department of Endocrinology and Metabolism, Qilu Hospital, Shandong University, Jinan, China.

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This summary is machine-generated.

A new deep learning tool automates corneal nerve fiber analysis from corneal confocal microscopy (CCM) images for early diabetic peripheral neuropathy (DPN) detection. This automated method shows high consistency with manual analysis, aiding in DPN diagnosis.

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Artificial intelligencecorneal confocal microscopedeep learningdiabetic neuropathy

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Area of Science:

  • Ophthalmology
  • Neurology
  • Medical Imaging

Background:

  • Diabetic peripheral neuropathy (DPN) is a common diabetes complication requiring early detection.
  • Corneal confocal microscopy (CCM) offers non-invasive assessment of corneal nerve fibers (CNFs) for DPN diagnosis.
  • Existing CNF analysis methods have limitations, necessitating automated solutions.

Purpose of the Study:

  • To develop and validate a deep learning-based automated tool for segmenting and quantifying CNF parameters from CCM images.
  • To assess the performance of the automated tool against manual annotations and existing methods like ACCMetrics.
  • To evaluate the tool's potential for early DPN diagnosis.

Main Methods:

  • Trained and evaluated deep learning (DL) models for CCM image segmentation.
  • Developed an image processing algorithm for automated extraction and quantification of CNF morphological parameters.
  • Validated the tool using manual annotations, ACCMetrics, Bland-Altman analysis, and intraclass correlation coefficient (ICC).

Main Results:

  • The U2Net model achieved the highest performance in CCM image segmentation (mIoU of 0.8115).
  • The automated tool demonstrated significantly higher consistency with manual results compared to ACCMetrics for CNF parameter quantification.
  • The tool achieved an area under the curve of 0.75 for DPN classification based on CNF morphology.

Conclusions:

  • The developed DL-based tool effectively segments and quantifies CNF parameters in CCM images.
  • This automated tool shows promise for the early diagnosis of DPN.
  • Further clinical validation is needed to confirm the practical application value of this tool.