Ruben Horst Duque, Carla Vasconcelos Cáspar Andrade, Valdir Ribeiro Campos, Isac Ribeiro Moulaz, Laíssa Fiorotti Albertino, Maria Bernadete Renoldi de Oliveira Gavi
Cross-sectional study of psychiatric disorders in patients with chronic musculoskeletal pain and individuals without pain
Advances in rheumatology (London, England)
deep learning increasingly investigated assisting clinical vitro fertilization ivf first technical step many tasks visually detect locate sperm oocytes embryos images clinical deployment deep learning models different clinics use different image acquisition hardware different sample preprocessing protocols raising concern whether reported accuracy deep learning model one clinic could reproduced another clinic aim investigate effect imaging factor generalizability object detection models using sperm analysis pilot example ablation studies performed using stateoftheart models detecting human sperm quantitatively assess model precision falsepositive detection recall missed detection affected imaging magnification imaging mode sample preprocessing protocols results led hypothesis richness image acquisition conditions training dataset deterministically affects model generalizability hypothesis tested first enriching training dataset wide range imaging conditions validated internal blind tests new samples external multicenter clinical validations ablation experiments revealed removing subsets data training dataset significantly reduced model precision removing raw sample images training dataset caused largest drop model precision whereas removing 20x images caused largest drop model recall incorporating different imaging sample preprocessing conditions rich training dataset model achieved intraclass correlation coefficient icc 097 95 ci 094099 precision icc 097 95 ci 093099 recall multicenter clinical validation showed significant differences model precision recall across different clinics applications results validated hypothesis richness data training dataset key factor impacting model generalizability findings highlight importance diversity training dataset model evaluation suggest future deep learning models andrology reproductive medicine incorporate comprehensive feature sets enhanced generalizability across clinics