AI-BASED - BIOMARKERS FOR EARLY DIAGNOSIS OF DIABETIC PERIPHERAL NEUROPATHY: EMERGING ROLE OF MACHINE LEARNING AND DIGITAL DIAGNOSTICS

Authors

  • Telmanova Navbakhar Telmanova Navbakhar Author
  • Parpiyeva Yulduz Ravshanovna Parpiyeva Yulduz Ravshanovna Author

Keywords:

Diabetic peripheral neuropathy; artificial intelligence; machine learning; digital biomarkers; early diagnosis

Abstract

Diabetic peripheral neuropathy (DPN) is one of the most common and disabling complications of diabetes mellitus. Early diagnosis remains difficult because clinical symptoms frequently appear only after irreversible nerve damage has already developed. Recent advances in artificial intelligence (AI), machine learning, and digital diagnostic technologies have created new opportunities for earlier detection of DPN. AI-based biomarkers derived from retinal imaging, corneal confocal microscopy, wearable technologies, and laboratory parameters may improve diagnostic precision and predictive accuracy. This review summarizes current evidence regarding the role of AI technologies in the early diagnosis of diabetic neuropathy and discusses the future potential of machine learning in preventive neurology and personalized medicine.

Author Biographies

  • Telmanova Navbakhar, Telmanova Navbakhar

    Department of Neurology and Medical Psychology, Tashkent State Medical University, Tashkent, Uzbekistan

  • Parpiyeva Yulduz Ravshanovna, Parpiyeva Yulduz Ravshanovna

    Department of Neurology and Medical Psychology, Tashkent State Medical University, Tashkent, Uzbekistan

References

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Published

2026-05-21

Issue

Vol. 6 No. 1 (2026): WORLD CONFERENCE ON MODERN RESEARCH APPROACHES

Section

Articles