| Revista: | Journal of the Mexican Federation of Radiology and Imaging |
| Base de datos: | |
| Número de sistema: | 000606801 |
| ISSN: | 2696-8444 |
| Autores: | Delsol Pérez, Claudia M1 Reyes Mosqueda, Alix D1 Ríos Rodríguez, Tania A1 Pérez Montemayor, David F1 |
| Instituciones: | 1Centro de Imagenologia Integral IMAX, Tampico, Tamaulipas. México |
| Año: | 2024 |
| Periodo: | Ene-Mar |
| Volumen: | 3 |
| Número: | 2 |
| Paginación: | 122-127 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | Artificial intelligence (AI) has been proposed as a tool for assessing mammographic breast density (MBD). This study aimed to evaluate the agreement of MBD classification between four radiologists (human readers [HRs]) with different years of experience in breast imaging and the AI Lunit INSIGHT MMG. This cross-sectional study was conducted with a convenience sample of radiologists trained in breast imaging who assessed MBD screening mammograms of asymptomatic women 35 years or older using BI-RADS descriptors. Cohens kappa determined the agreement between the HRs and AI. A total of 192 women with a mean age of 55.4 ± 31.8 years (range 37-82 years) were included. Interobserver agreement between HRs and AI varied in Category a but was substantial in Category b (HR1 k = 0.729, HR2 k = 0.718, HR3 k = 0.768, and HR4 k = 0.672) and in Category c, HR1, HR2, and HR3 had substantial agreement (k = 0.728, k = 0.697, and k = 0.738, respectively) and HR4 had moderate agreement (k = 0.578), while in Category d, it was mostly moderate. HRs and AI agreements varied from fair to substantial. HRs with more years of experience in breast image interpretation had a lower agreement with AI for MBD classification than HRs with less time. |
| Disciplinas: | Medicina, Medicina |
| Palabras clave: | Ginecología y obstetricia, Diagnóstico |
| Keyword: | Gynecology and obstetrics, Diagnosis |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
