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Abstract Artificial intelligence as a screening tool for eyelid lesions will be helpful for early diagnosis of eyelid malignancies and proper decision-making. This study aimed to evaluate the performance of a deep learning model in differentiating eyelid lesions using clinical eyelid photographs in comparison with human ophthalmologists. We included 4954 photographs from 928 patients in this retrospective cross-sectional study. Images were classified into three categories: malignant lesion, benign lesion, and no lesion. Two pre-trained convolutional neural network (CNN) models, DenseNet-161 and EfficientNetV2-M architectures, were fine-tuned to classify images into three or two (malignant versus benign) categories. For a ternary classification, the mean diagnostic accuracies of the CNNs were 82.1% and 83.0% using DenseNet-161 and EfficientNetV2-M, respectively, which were inferior to those of the nine clinicians (87.0–89.5%). For the binary classification, the mean accuracies were 87.5% and 92.5% using DenseNet-161 and EfficientNetV2-M models, which was similar to that of the clinicians (85.8–90.0%). The mean AUC of the two CNN models was 0.908 and 0.950, respectively. Gradient-weighted class activation map successfully highlighted the eyelid tumors on clinical photographs. Deep learning models showed a promising performance in discriminating malignant versus benign eyelid lesions on clinical photographs, reaching the level of human observers.

Abstract The aim of this study is to characterize the microRNA (miRNA) expression signatures in patients with thyroid eye disease (TED) and identify miRNA biomarkers of disease activity. Total RNA was isolated from the sera of patients with TED (n = 10) and healthy controls (HCs, n = 5) using the miRNeasy Serum/Plasma Kit. The NanoString assay was used for the comprehensive analysis of 798 miRNA expression profiles. Analysis of specific miRNA signatures, mRNA target pathway analysis, and network analysis were performed. Patients with TED were divided into two groups according to disease activity: active and inactive TED groups. Differentially expressed circulating miRNAs were identified and tested using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) tests in the validation cohort. Among the 798 miRNAs analyzed, 173 differentially downregulated miRNAs were identified in TED patients compared to those in the HCs. Ten circulating miRNAs were differentially expressed between the active and inactive TED groups and regarded as candidate biomarkers for TED activity (one upregulated miRNA: miR-29c-3p; nine downregulated miRNAs: miR-4286, miR-941, miR-571, miR-129-2-3p, miR-484, miR-192-5p, miR-502-3p, miR-597-5p, and miR-296-3p). In the validation cohort, miR-484 and miR-192-5p showed significantly lower expression in the active TED group than in the inactive TED group. In conclusion, the expression levels of miR-484 and miR-192-5p differed significantly between the active and inactive TED groups, suggesting that these miRNAs could serve as circulating biomarkers of TED activity, however, these findings need to be validated in further studies.

Abstract Although the clinical activity score (CAS) is a validated scoring system for identifying disease activity of thyroid-associated orbitopathy (TAO), it may produce differing results depending on the evaluator, and an experienced ophthalmologist is required for accurate evaluation. In this study, we developed a machine learning (ML)-assisted system to mimic an expert’s CAS assessment using digital facial images and evaluated its accuracy for predicting the CAS and diagnosing active TAO (CAS ≥ 3). An ML-assisted system was designed to assess five CAS components related to inflammatory signs (redness of the eyelids, redness of the conjunctiva, swelling of the eyelids, inflammation of the caruncle and/or plica, and conjunctival edema) in patients’ facial images and to predict the CAS by considering two components of subjective symptoms (spontaneous retrobulbar pain and pain on gaze). To train and test the system, 3,060 cropped images from 1020 digital facial images of TAO patients were used. The reference CAS for each image was scored by three ophthalmologists, each with > 15 years of clinical experience. We repeated the experiments for 30 randomly split training and test sets at a ratio of 8:2. The sensitivity and specificity of the ML-assisted system for diagnosing active TAO were 72.7% and 83.2% in the test set constructed from the entire dataset. For the test set constructed from the dataset with consistent results for the three ophthalmologists, the sensitivity and specificity for diagnosing active TAO were 88.1% and 86.9%. In the test sets from the entire dataset and from the dataset with consistent results, 40.0% and 49.9% of the predicted CAS values were the same as the reference CAS, respectively. The system predicted the CAS within 1 point of the reference CAS in 84.6% and 89.0% of cases when tested using the entire dataset and in the dataset with consistent results, respectively. An ML-assisted system estimated the clinical activity of TAO and detect inflammatory active TAO with reasonable accuracy. The accuracy could be improved further by obtaining more data. This ML-assisted system can help evaluate the disease activity consistently as well as accurately and enable the early diagnosis and timely treatment of active TAO.