No hay miniatura disponible
Fecha
2024-05-22
Editor/a
Director/a
Tutor/a
Coordinador/a
Prologuista
Revisor/a
Ilustrador/a
Derechos de acceso
info:eu-repo/semantics/openAccess
Título de la revista
ISSN de la revista
Título del volumen
Editor
Elsevier
Citas
0 citas en 
0 citas en 
Resumen
Aims
Machine learning models can use image and text data to predict the number of years since diabetes diagnosis; such model can be applied to new patients to predict, approximately, how long the new patient may have lived with diabetes unknowingly. We aimed to develop a model to predict self-reported diabetes duration.
Methods
We used the Brazilian Multilabel Ophthalmological Dataset. Unit of analysis was the fundus image and its meta-data, regardless of the patient. We included people 40 + years and fundus images without diabetic retinopathy. Fundus images and meta-data (sex, age, comorbidities and taking insulin) were passed to the MedCLIP model to extract the embedding representation. The embedding representation was passed to an Extra Tree Classifier to predict: 0–4, 5–9, 10–14 and 15 + years with self-reported diabetes.
Results
There were 988 images from 563 people (mean age = 67 years; 64 % were women). Overall, the F1 score was 57 %. The group 15 + years of self-reported diabetes had the highest precision (64 %) and F1 score (63 %), while the highest recall (69 %) was observed in the group 0–4 years. The proportion of correctly classified observations was 55 % for the group 0–4 years, 51 % for 5–9 years, 58 % for 10–14 years, and 64 % for 15 + years with self-reported diabetes.
Conclusions
The machine learning model had acceptable accuracy and F1 score, and correctly classified more than half of the patients according to diabetes duration. Using large foundational models to extract image and text embeddings seems a feasible and efficient approach to predict years living with self-reported diabetes.
Descripción
The registered version of this article, first published in Primary Care Diabetes, is available online at the publisher's website: Elsevier, https://doi.org/10.1016/j.pcd.2024.04.002
La versión registrada de este artículo, publicado por primera vez en Primary Care Diabetes, está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.pcd.2024.04.002
La versión registrada de este artículo, publicado por primera vez en Primary Care Diabetes, está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.pcd.2024.04.002
Categorías UNESCO
Palabras clave
deep learning, artificial intelligence, non-communicable diseases
Citación
Carrillo-Larco, R. M., Bravo-Rocca, G., Castillo-Cara, M., Xu, X., & Bernabe-Ortiz, A. (2024). A multimodal approach using fundus images and text meta-data in a machine learning classifier with embeddings to predict years with self-reported diabetes–An exploratory analysis. Primary Care Diabetes.
Centro
E.T.S. de Ingeniería Informática
Departamento
Inteligencia Artificial
Grupo de investigación
Sistemas inteligentes de ayuda a la decisión