Abstract <p>Homologous recombination deficiency (HRD) is a critical biomarker in high-grade serous ovarian cancer for the clinical benefit from platinum-based chemotherapy and poly polymerase inhibitors, but molecular testing is costly, time-consuming, and limited by tissue requirements. In this study, we introduce dPathHRD (digital Pathological assessment of Homologous Recombination Deficiency), a deep learning model designed to predict HRD status and platinum chemotherapy response directly from routine hematoxylin and eosin-stained whole-slide images. By integrating a pre-trained transformer-based pathology foundation model with an attention-based multiple-instance learning architecture, dPathHRD successfully predicts HRD status with an area under the curve of 0.920 in the discovery cohort and 0.766 in the validation cohort. The digital scores generated by dPathHRD were significantly correlated with established HRD-related genomic and transcriptomic features.&#xa0;Furthermore, dPathHRD demonstrated the ability to predict therapeutic response to platinum chemotherapy, with the HRD-like group showing higher complete response rates and longer progression-free and recurrence-free survival compared to the homologous recombination proficiency (HRP)-like group across all three cohorts. Interpretation analysis via attention mapping confirmed the model’s reliance on biologically relevant histopathological features in tumor and stromal regions. In conclusion, dPathHRD offers a promising, cost-effective alternative to molecular testing, leveraging widely available digital pathology images to inform personalized treatment strategies. Further prospective validation is warranted to confirm its clinical applicability and predictive power.</p> Graphical Abstract <p></p>