Classification of salivary gland biopsies in Sjögren’s syndrome by a convolutional neural network using an auto-machine learning platform
AutoML platforms offer a rapid and flexible approach to developing machine learning models, even with smaller datasets, as demonstrated in this study for SS. These platforms hold significant potential for enhancing diagnostic precision and efficiency in both clinical and research settings. Multicentric studies with larger patient cohorts are essential for thorough evaluation and validation of this innovative diagnostic approach.
Authors
- Thomas Hügle (ORCID: https://orcid.org/0000-0002-3276-9581)
- Clara Soto Abánades
- Jorge Álvarez Troncoso (ORCID: https://orcid.org/0000-0003-1108-9461)
- Elena Ruiz‐Bravo
- Alexandre Dumusc (ORCID: https://orcid.org/0000-0002-3773-357X)
- Álvaro López‐Janeiro (ORCID: https://orcid.org/0000-0002-5744-3115)
Institutions
- Hospital Universitario La Paz (ES)
- Centre Hospitalier Universitaire Vaudois (CH)
- Hôpital Orthopédique de la Suisse Romande (CH)
- Clinica Universidad de Navarra (ES)
- University of Lausanne (CH)
Publication Details
- Journal
- BMC Rheumatology
- Published
- 2024-11-06
- DOI
- https://doi.org/10.1186/s41927-024-00417-3
- Citations
- 11
- Primary Topic
- Salivary Gland Disorders and Functions
- Type
- article
- Field-Weighted Citation Impact
- 3.59
Funders
- Université de Lausanne