A new digital biomarker of Demodex blepharitis: energy curve of the meibomian edge - Wang, Minjia; Chen, Xiaoyu; Wang, Kesheng; Xu, Kunhui; Yu, Xinxin; Dai, Qi; Ren, Min (Frontiers in Cell and Developmental Biology, 2025)
PurposeTo develop and validate a novel digital biomarker, the energy curve of the meibomian gland (MG) edge, to assess MG uneven atrophy and aid in diagnosing Demodex blepharitis.MethodsA retrospective study enrolled 76 dry eye patients (42 with Demodex blepharitis, 34 controls). Segmentation of upper eyelid meibography images was accomplished via a convolutional neural network (CNN)-based artificial intelligence (AI) model. The lower margin curve of MGs was extracted using an active contour model (Snake) to compute a composite energy value that integrates elastic, curvature, and smoothness energies. Clinical parameters, including non-invasive tear breakup time (NIBUT), lid margin score, and Meiboscore, were evaluated.ResultsThe Demodex group showed shorter NIBUT (median: 2.84 vs. 5.18 s, p < 0.001) and higher lid margin scores (median: 2 vs. 1, p = 0.002) and Meiboscores (median: 1 vs. 1, p = 0.009). The Demodex group also exhibited significantly higher energy curve values than controls (median: 32.44 vs. 11.20, p < 0.001), reflecting pronounced uneven gland atrophy. Meanwhile, MG density significantly influenced energy curve values (p = 0.010). After adjusting for MG density, the energy curve demonstrated strong diagnostic accuracy (AUC = 0.897, sensitivity 78.6%, specificity 91.2%).ConclusionThe energy curve quantifies structural irregularities in MGs caused by Demodex infestation, serving as a non-invasive biomarker for early diagnosis. Its integration with meibography enhances clinical workflows, particularly in resource-limited settings.