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Explainable Artificial Intelligence Approaches for Autism Spectrum Disorder Classification via Hyperspectral Imaging Analysis of Red Blood Cells

Year: 2026

Type: article

Abstract: Objective biological markers for Autism Spectrum Disorder (ASD) remain an important research goal because clinical assessment is behavior-based and ASD is heterogeneous. This study evaluated whether eight red-blood-cell (RBC) hyperspectral endmember distributions contain participant-level ASD-versus-neurotypical classification signal under leakage-controlled validation. The cohort comprised 58 male children (27 ASD and 31 neurotypically developing), each with nine RBC hyperspectral acquisitions (522 acquisitions in total). Six classifiers were evaluated using 58-fold outer leave-one-subject-out cross-validation, with five-fold participant-grouped inner hyperparameter selection. The nine held-out probabilities were averaged to obtain one prediction per participant. XGBoost achieved the highest observed fixed-threshold accuracy (0.828; 95% CI 0.724–0.914), F1-score (0.800; 0.667–0.906), Matthews correlation coefficient (0.657), and lowest Brier score (0.155). Support Vector Machine achieved the highest AUROC (0.879; 0.784–0.952), average precision (AP; 0.891), and specificity (0.968), but lower sensitivity (0.630). Exact paired McNemar tests found no significant participant-level accuracy difference between XGBoost and the other models (all $p\geq 0.109$ ). Out-of-fold TreeSHAP identified Spectrum 1, Spectrum 2, Spectrum 5, and Spectrum 4 as the leading XGBoost features. These model-specific associations are hypothesis-generating and do not establish molecular or causal specificity. The findings support further investigation of RBC hyperspectral profiles, but the small, male-only, single-cohort design and absence of external validation preclude claims of clinical generalizability or diagnostic readiness.

Source: IEEE Access

DOI: 10.1109/access.2026.3736887