Deep Learning-Based Classification of Slit-Lamp Photograph Quality in Microbial Keratitis.
Microbial keratitis (MK) is one of the leading causes of blindness in low- and middle-income countries that often requires timely diagnosis and subsequent treatment. Literature has shown that poor-quality slit-lamp photos (SLPs) can negatively impact artificial intelligence algorithm performance in keratitis classification. In this study, we develop and validate a deep learning (DL) model to assess SLP quality in MK.Deep learning training and validation on a novel image dataset of MK eyes from a prospective clinical study.Slit lamp photos of MK with 4 illumination types between July 23, 2020, and May 1, 2024, prospectively collected during the Automated Quantitative Ulcer Analysis study.Slit lamp photo quality was classified as either "good" or "poor" based on a standardized grading protocol. Five DL-based classification models (AlexNet, ResNet50, DenseNet169, InceptionV3, and MobileNetV2) were trained and evaluated using fivefold cross validation. Model performance was visualized with gradient-weighted class activation mapping.Accuracy metrics of the model include accuracy and F1-score, which is harmonic mean of precision and recall. Gradient-weighted class activation mapping heatmap distribution of the model was also assessed.We collected a range of 247 to 264 images for each illumination type from 138 individuals. The mean age was 54.0 ± 19.1 years, 58.8% were female, 89.9% were White, and 4.6% were Hispanic/Latino. The proportion of good-quality images varied by illumination types: 63% for diffuse white light (158/252), 51% for diffuse blue light (125/247), 42% for sclerotic scatter (108/256), and 23% for slit beam (61/264). MobileNetV2 achieved the highest performance in predicting SLP quality, with accuracy scores of 83.73 ± 6.02% (81.56 ± 9.16% F1-score) for diffuse white light, 79.79 ± 3.83% (79.13 ± 4.86% F1-score) for diffuse blue light, 79.91 ± 4.47% (68.63 ± 20.06% F1-score) for slit beam illumination, and 71.88 ± 4.69% (and 69.64 ± 10.34% F1-score) for sclerotic scatter.This study highlights the complexity of assessing SLP quality in MK. Future research is needed to examine how image quality affects automated decision-making in MK.Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
