Smartphone-Coupled Phase Contrast Microscopy Combined with Deep Transfer Learning for Candida Species Identification: A Proof-of-Concept Study

Abstract

Species-level Candida identification can inform antifungal management, but reliable identification platforms remain inaccessible in many clinical microbiology laboratories, whereas phase contrast microscopy -- a common feature of routine laboratory microscopes -- is widely available. We asked whether this ubiquitous optical tool, combined with a consumer smartphone and deep transfer learning, could provide a feasible low-cost approach for preliminary Candida species discrimination. Fifteen clinical isolates of four species (C. albicans, C. glabrata, C. tropicalis, C. krusei) were collected from a single clinical microbiology laboratory and imaged using a consumer-grade smartphone coupled to a standard phase contrast microscope. Suspensions in human serum were imaged immediately after preparation (T0) and after 2-hour incubation at 37{degrees}C (T2). Pretrained vision backbone architectures were evaluated as fixed feature extractors under strict Leave-One-Strain-Out cross-validation. The best-performing model -- EfficientNet-B0 embeddings with a Linear Support Vector Machine applied to T2 images -- achieved an apparent internally cross-validated strain-level balanced accuracy of 0.833 and an overall strain accuracy of 86.7% (13/15 strains correctly classified). C. albicans, C. glabrata, and C. tropicalis were each identified with 100% recall. Both misclassified strains belonged to C. krusei -- the species with the smallest panel representation (n=3 strains) -- with misclassification attributable to limited strain diversity and suboptimal image quality. These findings demonstrate promising feasibility for preliminary image-based Candida species discrimination from smartphone-acquired phase contrast microscopy images, and support further evaluation in larger, externally validated strain collections.