Application of an Interpretable Machine Learning for Estimating Severity of Graves’ Orbitopathy Based on Initial Finding

Abstract

(1) Background: We constructed scores for moderate-to-severe and muscle-predominant types of Graves’ orbitopathy (GO) risk prediction based on initial ophthalmic findings. (2) Methods: 400 patients diagnosed with GO and followed up at both endocrinology and ophthalmology clinics with at least 6 months of follow-up. The Score for Moderate-to-Severe type of GO risk Prediction (SMSGOP) and the Score for Muscle-predominant type of GO risk Prediction (SMGOP) were constructed using the machine learning-based automatic clinical score generation algorithm. (3) Results: 55.3% were classified as mild type and 44.8% were classified as moderate-to-severe type. In the moderate-to-severe type group, 32.3% and 12.5% were classified as fat-predominant and muscle-predominant type, respectively. SMSGOP included age, central diplopia, thyroid stimulating immunoglobulin, modified NOSPECS classification, clinical activity score and ratio of the inferior rectus muscle cross-sectional area to total orbit in initial examination. SMGOP included age, central diplopia, amount of eye deviation, serum FT4 level and the interval between diagnosis of GD and GO in initial examination. Scores ≥46 and ≥49 had predictive value, respectively. (4) Conclusions: This is the first study to analyze factors in initial findings that can predict the severity of GO and to construct scores for risk prediction for Korean. We set the predictive scores using initial findings.

Advances in artificial intelligence in thyroid-associated ophthalmopathy

“…Additionally, Lee et al. ( 43 ) created an automated clinical scoring algorithm using ML to predict TAO severity and type comprehensively. AI-based methods for TAO treatment decision-making are transforming clinical practice by furnishing personalized and precise options ( 47 ).…”

“…Lee et al. ( 43 ) conducted a study involving 400 patients diagnosed with TAO and developed an automated clinical scoring algorithm using ML. This algorithm quantified the risk of developing moderate-to-severe TAO or muscle-dominant TAO and achieved better model performance than PREDIGO which was proposed by Wiersinga et al.…”