Data from: Advancing mold identification in the routine laboratory: Performance of smartphone-based imaging and a newly developed Convolutional Neural Network

Background: Mold identification in clinical diagnostics is traditionally
labor intensive and is dependent on expert interpretation. MoldVision is a
deep learning approach that uses smartphone images ofmold cultures to
automate identification. Methods: We analyzed 161 clinical isolates across
four common mold genera. Penicillium spp., Aspergillus spp. (with A.
flavus and A. fumigatus), Fusarium spp., and Cladosporium spp. Daily
images were captured from the top and bottom of culture plates over five
days using a standardized smartphone setup, generating over 4,000 images.
We trained three variations of VGG16 convolutional neural networks (CNN)
and benchmarked the best-performing model (VGG16 with dual classification
heads) against LightGBM models trained on pre-extracted features and human
expert assessments at various time points. Results: The best performing
VGG16 model achieved a mean (SD) ROC-AUC of 92.7% ± 1.8% and sensitivity
of 68.7% ±2.6% across all species. Here, the performance in identifying
Cladosporium spp. was best (ROC-AUC 99.9% ± 0.1%, 5-fold cross-validation
mean and SD ). Regarding the evaluations over time, early stage
classification (days 1-2) was challenging (F1-score 38.8% ± 3.5% across
all species but improved significantly on day 3-5 (F1 92.1% ± 2.8% across
all species). Compared to experts, MoldVision consistently showed superior
performance, particularly in mature cultures, detecting subtle
morphological features earlier and more accurately. Conclusions: Our
results demonstrate that CNNs integrated with low-cost smartphone imaging
can reliably classify mold species in routine diagnostics, outperforming
human experts in many cases. This approach offers a practical and scalable
solution for laboratories lacking specialized mycology expertise,
especially in resource-limited settings.