Can an artificial intelligence powered software reliably assess pelvic radiographs?

Abstract

Purpose Despite advances of three-dimensional imaging pelvic radiographs remain the cornerstone in the evaluation of the hip joint. However, large inter- and intra-rater variabilities were reported due to subjective landmark setting. Artificial intelligence (AI)–powered software applications could improve the reproducibility of pelvic radiograph evaluation by providing standardized measurements. The aim of this study was to evaluate the reliability and agreement of a newly developed AI algorithm for the evaluation of pelvic radiographs. Methods Three-hundred pelvic radiographs from 280 patients with different degrees of acetabular coverage and osteoarthritis (Tönnis Grade 0 to 3) were evaluated. Reliability and agreement between manual measurements and the outputs of the AI software were assessed for the lateral-center-edge (LCE) angle, neck-shaft angle, sharp angle, acetabular index, as well as the femoral head extrusion index. Results The AI software provided reliable results in 94.3% (283/300). The ICC values ranged between 0.73 for the Acetabular Index to 0.80 for the LCE Angle. Agreement between readers and AI outputs, given by the standard error of measurement (SEM), was good for hips with normal coverage (LCE-SEM: 3.4°) and no osteoarthritis (LCE-SEM: 3.3°) and worse for hips with undercoverage (LCE-SEM: 5.2°) or severe osteoarthritis (LCE-SEM: 5.1°). Conclusion AI-powered applications are a reliable alternative to manual evaluation of pelvic radiographs. While being accurate for patients with normal acetabular coverage and mild signs of osteoarthritis, it needs improvement in the evaluation of patients with hip dysplasia and severe osteoarthritis.

Gender‐specific distribution of knee morphology according to CPAK and functional phenotype classification: analysis of 8739 osteoarthritic knees prior to total knee arthroplasty using artificial intelligence

“…The use of automated applications with Artificial Intelligence has shown good results in musculoskeletal radiology lately, and enables the analysis of big data sets in a standardized way [19, 29, 40].…”

Uso de inteligencia artificial como soporte para el aprendizaje en las ciencias de la salud

“…Se realizó un estudio en EE.UU. donde se investigó la confiabilidad de la inteligencia artificial y el efecto que ha tenido dentro de los hospitales y centros educativos, presentando un índice de confianza del 59% en la precisión y enfoque de la inteligencia artificial dentro del campo médico, para la implementación de prácticas con simuladores virtuales fortaleciendo el aprendizaje de estudiantes y personal de salud (Lino Solís et al, 2021;Schwarz et al, 2023).…”

“…En el campo imagenológico en donde Gilbert M Schwarz ha implementado el software de inteligencia artificial para lecturas manuales de resultados imagenológicos, generó resultados positivos y seguros, obteniendo un total del 94% en estudios radiológicos de pelvis, lo cual ha permitiendo que los estudiantes puedan aprender a identificar de mejor manera el diagnóstico del paciente y el abordaje que debe ser realizado con cada uno de los mismos (Schwarz et al, 2023). Otros autores mencionan que la inteligencia artificial brinda un enfoque integral, pues permite que el estudiante pueda evaluar anomalías precisas en estudios de imagen, procesos quirúrgicos, análisis de muestras y fortalecer las prácticas médicas (Álvarez Vega et al, 2020).…”