Bridging Electrophysiology and Digital Health: Microneurography Findings in Long <scp>COVID</scp> Herald a New Era of <scp>AI</scp> ‐Powered Peripheral Nerve Monitoring

The innovative microneurography results by Ribeiro et al revealing objective small-fiber nerve damage in patients with long coronavirus disease (COVID) signify more than just diagnostic validation; they indicate a transformative shift toward artificial intelligence (AI)-driven digital biomarkers for peripheral neuropathy.1 Their evidence of spontaneous nociceptor activity, peripheral sensitization, and modified C-nociceptor populations establishes the essential physiological basis for creating innovative wearable monitoring devices. Traditional diagnosis depends on invasive skin biopsies and subjective symptom reporting; however, the electrophysiological patterns discerned via microneurography present a distinctive possibility of creating non-invasive digital surrogates. The 61.1% prevalence of spontaneous nociceptor activity and the notable transition to mechano-insensitive C-nociceptors provide quantifiable physiological fingerprints that may be detected using advanced wearable technologies featuring intricate sensor arrays.1 Recent advancements in wearable device technologies exhibit significant proficiency in monitoring autonomic dysfunction, a characteristic of long COVID.2 Continuous collection of heart rate variability, sleep patterns, and physical activity parameters via US Food and Drug Administration (FDA)-approved devices, such as smartwatches, offers insights into nervous system dysfunction.2 We propose a groundbreaking advancement: training AI algorithms on the precise electrophysiological patterns found by microneurography to develop predictive models capable of detecting and monitoring tiny nerve fiber dysfunction through passive digital biomarkers. The integration of AI with peripheral nerve diagnostics has demonstrated remarkable potential. Deep learning models exhibit nearly flawless accuracy in diagnosing diabetic peripheral neuropathy by corneal confocal microscopy, attaining 97% (as reported) precision in automated nerve fiber quantification.3 Machine learning methodologies effectively forecast peripheral neuropathy by the use of pressure sensor-integrated insoles and gamified evaluations, with an accuracy of 81.7% in identifying nerve dysfunction.4 These precedents indicate that integrating microneurography-derived physiological insights with AI-enhanced wearable monitoring could transform long COVID management. The clinical ramifications encompass not only diagnostics but also precision medicine. By establishing individual baseline patterns of small-fiber activity via wearables, doctors can identify early degeneration, assess therapy response, and tailor therapeutic approaches. This method tackles the significant deficiency in long COVID management—the absence of objective, continuous monitoring instruments for this variable, diverse illness. This digital biomarker methodology could facilitate equitable access to professional neurophysiological evaluations. Although microneurography requires specialized expertise and technology exclusive to tertiary centers, AI-driven wearable algorithms could facilitate advanced nerve function monitoring in primary care environments and patients’ residences. Scalability is essential due to the millions of patients with long COVID globally necessitating continuous neurological monitoring.5 The societal impact may be revolutionary. The real-time identification of small-fiber dysfunction by wearables may facilitate early management, avert irreparable nerve damage, and potentially halt the transition from acute illness to chronic impairment. Furthermore, the collection of population-level data via these digital biomarkers could expedite research into the pathogenesis of long COVID and the development of therapies. We encourage the research community to leverage these microneurography discoveries to create AI-driven digital biomarkers for small nerve fiber disorders. This signifies a distinctive opportunity to revolutionize peripheral neuropathy monitoring from sporadic, invasive evaluations to continuous, patient-focused care via wearable technology. The physiological foundation has been established; now we must construct the digital infrastructure to democratize and transform nerve function monitoring. Shaher Yar: Conceptualization; writing – original draft; visualization; writing – review and editing; project administration; resources; supervision. Reza Rafieossadat: Writing – review and editing; supervision. Parvat Kuwar Chhetri: Writing – original draft; writing – review and editing; validation.