Your browser doesn't support javascript.
loading
Automated reporting of cervical biopsies using artificial intelligence.
Mohammadi, Mahnaz; Fell, Christina; Morrison, David; Syed, Sheeba; Konanahalli, Prakash; Bell, Sarah; Bryson, Gareth; Arandjelovic, Ognjen; Harrison, David J; Harris-Birtill, David.
Afiliación
  • Mohammadi M; School of Computer Science, University of St Andrews, St Andrews, United Kingdom.
  • Fell C; School of Computer Science, University of St Andrews, St Andrews, United Kingdom.
  • Morrison D; School of Computer Science, University of St Andrews, St Andrews, United Kingdom.
  • Syed S; Department of Pathology, Queen Elizabeth University Hospital, Glasgow, United Kingdom.
  • Konanahalli P; Department of Pathology, Queen Elizabeth University Hospital, Glasgow, United Kingdom.
  • Bell S; Department of Pathology, Queen Elizabeth University Hospital, Glasgow, United Kingdom.
  • Bryson G; Department of Pathology, Queen Elizabeth University Hospital, Glasgow, United Kingdom.
  • Arandjelovic O; School of Computer Science, University of St Andrews, St Andrews, United Kingdom.
  • Harrison DJ; School of Medicine, University of St Andrews, United Kingdom.
  • Harris-Birtill D; Pathology, Division of Laboratory Medicine, Royal Infirmary of Edinburgh, United Kingdom.
PLOS Digit Health ; 3(4): e0000381, 2024 Apr.
Article en En | MEDLINE | ID: mdl-38648217
ABSTRACT
When detected at an early stage, the 5-year survival rate for people with invasive cervical cancer is 92%. Being aware of signs and symptoms of cervical cancer and early detection greatly improve the chances of successful treatment. We have developed an Artificial Intelligence (AI) algorithm, trained and evaluated on cervical biopsies for automated reporting of digital diagnostics. The aim is to increase overall efficiency of pathological diagnosis and to have the performance tuned to high sensitivity for malignant cases. Having a tool for triage/identifying cancer and high grade lesions may potentially reduce reporting time by identifying areas of interest in a slide for the pathologist and therefore improving efficiency. We trained and validated our algorithm on 1738 cervical WSIs with one WSI per patient. On the independent test set of 811 WSIs, we achieved 93.4% malignant sensitivity for classifying slides. Recognising a WSI, with our algorithm, takes approximately 1.5 minutes on the NVIDIA Tesla V100 GPU. Whole slide images of different formats (TIFF, iSyntax, and CZI) can be processed using this code, and it is easily extendable to other formats.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: PLOS Digit Health Año: 2024 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: PLOS Digit Health Año: 2024 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Estados Unidos