FetalMLOps: operationalizing machine learning models for standard fetal ultrasound plane classification.
Authors
Affiliations (8)
Affiliations (8)
- Artificial Venture Builder, London, UK. [email protected].
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy.
- Department of Management, Information, and Production Engineering, Università di Bergamo, Bergamo, Italy.
- Department of Computer Science, Università di Bologna, Bologna, Italy.
- Unit of Medical Statistics and Molecular Epidemiology, Università Campus Bio-Medico Roma, Rome, Italy.
- Department of Political Sciences, Communication, and International Relations, Università di Macerata, Macerata, Italy.
- Department of Innovative Technologies in Medicine and Dentistry, Università degli Studi "G. d'Annunzio" Chieti-Pescara, Chieti, Italy.
- Department of Computer Science, Università degli Studi di Bari Aldo Moro, Bari, Italy.
Abstract
Fetal standard plane detection is essential in prenatal care, enabling accurate assessment of fetal development and early identification of potential anomalies. Despite significant advancements in machine learning (ML) in this domain, its integration into clinical workflows remains limited-primarily due to the lack of standardized, end-to-end operational frameworks. To address this gap, we introduce FetalMLOps, the first comprehensive MLOps framework specifically designed for fetal ultrasound imaging. Our approach adopts a ten-step MLOps methodology that covers the entire ML lifecycle, with each phase meticulously adapted to clinical needs. From defining the clinical objective to curating and annotating fetal US datasets, every step ensures alignment with real-world medical practice. ETL (extract, transform, load) processes are developed to standardize, anonymize, and harmonize inputs, enhancing data quality. Model development prioritizes architectures that balance accuracy and efficiency, using clinically relevant evaluation metrics to guide selection. The best-performing model is deployed via a RESTful API, following MLOps best practices for continuous integration, delivery, and performance monitoring. Crucially, the framework embeds principles of explainability and environmental sustainability, promoting ethical, transparent, and responsible AI. By operationalizing ML models within a clinically meaningful pipeline, FetalMLOps bridges the gap between algorithmic innovation and real-world application, setting a precedent for trustworthy and scalable AI adoption in prenatal care.