Detection of Retinal Dysfunction with Multimodal PERG Analysis: A Patient-Level Hybrid Machine Learning Framework

Pattern electroretinogram (PERG) is the standard for assessing retinal ganglion cell function. However, the low amplitude and complex waveform of PERG signals complicate clinical interpretation. This study proposes a robust, multimodal hybrid machine learning framework that detects retinal dysfunction under a rigorous patient level validation strategy by integrating PERG waveform features with clinical demographic data. The PERG-IOBA dataset, consisting of 1354 signals from 304 participants was used. Training and test sets were separated at the patient level using 5-fold cross validation to approximate real clinical deployment and to avoid information leakage. A dual-stream model was developed. One stream processed functional PERG features, latency, amplitude and RMS via a multilayer perceptron, while the second stream processed clinical data. The two representations were then fused at the feature concatenation level. This model (Model 1) was compared with a stacking ensemble of conventional classifiers (Model 2) and a two-stage cascade classifier tailored for screening (Model 3). Model 2 achieved the most balanced and robust performance with 71.4% accuracy and an Area Under the Curve of 0.76 in 5-fold patient level cross validation. Although more modest than many previously reported values, these metrics are consistent with realistic clinical generalizability. The Model 3 provided the highest sensitivity with 79.7% for screening purposes. SHAP analysis confirmed P50-N95 amplitude as the primary biomarker but identified age as a significant confounding factor, mimicking expert clinical judgment. This study demonstrates that retinal dysfunction detection requires a whole approach that integrates signal morphology and patient demographics.