Dale, Erica

ABSTRACT Background Evaluating fairness in artificial intelligence (AI) is essential before clinical implementation to prevent unintended harm across diverse patient populations. This study assesses performance differences of an image-based surgical site infection (SSI) detection model across age, gender, geography, race, and skin color. Methods Model development/validation study using retrospective, multicenter data across surgical subspecialties from adults who submitted an image of their incision via the electronic health record within 30 days of their procedure between 2019-2024. Area under receiver operating curve (AUROC) was compared by age (<55 vs. ≥55 years old), gender (male vs. female), geography (Rochester, Minnesota vs. Jacksonville, Florida vs. Scottsdale, Arizona vs. regional Minnesota/Wisconsin areas), race (White/Caucasian vs. non-White/Caucasian), and skin color (Fitpatrick I-III vs. IV-VI and Monk A-E vs. F-J). Results Training data contained 13,702 images from 4,274 patients between 2019-2022 (50.1% ≥55 years old; 63.2% female; 91.6% White/Caucasian; 58.5% Rochester, 17.3% Florida, 10.5% Arizona; 96.3% light skin tone), of which 1,348 images (9.8%) had an SSI. No differences in SSI detection existed for age, gender, race, or skin color (AUROC 0.83-0.87, p>0.09), however geographical differences were significant: lowest AUROC 0.83 (95% CI 0.77-0.88) vs. highest AUROC 0.87 (95% CI 0.84-0.89, p=0.01). Temporal validation using 4,242 images from 1,353 patients between 2022-2023 showed intact fairness across subgroups (ΔAUROC ≤0.06) except geography (ΔAUROC ≤0.16). Conclusions The model behaved fairly across age, gender, race, and skin color, however geographical differences persisted. These findings have practical implications underscoring the importance of evaluating bias in clinical AI before deployment.

Biparietal osteodystrophy, also known as bilateral parietal calvarium bone thinning, is a rare calvarial condition characterized by loss of diploic bone within the parietal region. Although historically described in anthropologic and radiologic literature, current understanding of its epidemiology, pathogenesis, clinical relevance, and management remains fragmented. This review synthesizes current evidence and highlights emerging studies on biparietal osteodystrophy. Epidemiologic reports indicate that biparietal osteodystrophy affects a broad age range but appears most frequently in older adults, with several series suggesting a slight female predominance. Prevalence is difficult to estimate due to incidental detection, though modern imaging has increased recognition of the condition. Histologically and anatomically, biparietal osteodystrophy is marked by thinning of the outer table and loss of the diploic space, with little osteoblastic or osteoclastic remodeling. Proposed pathogenic mechanisms include chronic degenerative change, impaired vascular supply to the diploë, and other theories, although no single explanation fully accounts for its complete presentation. Clinically, most patients remain asymptomatic, with complications such as calvarial fractures and traumatic brain injury being rare but highlighting the structural vulnerability associated with severe thinning. Management strategies are not standardized, and the use of anti-osteoporotic drugs and cranioplasty has been reported to offer benefit in select cases, although no treatment has been formally validated for biparietal osteodystrophy. As the understanding of biparietal osteodystrophy continues to evolve, future research is needed to clarify its natural history, identify potential etiologic pathways, and establish evidence-based management guidelines.