TY - JOUR
T1 - A decision support system for the detection of cutaneous fungal infections using artificial intelligence
AU - Rappoport, Naama
AU - Rappoport, Naama
AU - Goldinger, Gil
AU - Debby, Assaf
AU - Molchanov, Yosef
AU - Barak, Yoash
AU - Barzilai, Aviv
AU - Rappoport, Naama
AU - Barzilai, Aviv
AU - Rappoport, Naama
AU - Barzilai, Aviv
AU - Gildenblat, Jacob
AU - Hadar, Ofir
AU - Sagiv, Chen
N1 - Publisher Copyright:
© 2024 Elsevier GmbH
PY - 2024/9
Y1 - 2024/9
N2 - Cutaneous fungal infections are one of the most common skin conditions, hence, the burden of determining fungal elements upon microscopic examination with periodic acid-Schiff (PAS) and Gomori methenamine silver (GMS) stains, is very time consuming. Despite some morphological variability posing challenges to training artificial intelligence (AI)-based solutions, these structures are favored potential targets, enabling the recruitment of promising AI-based technologies. Herein, we present a novel AI solution for identifying skin fungal infections, potentially providing a decision support system for pathologists. Skin biopsies of patients diagnosed with a cutaneous fungal infection at the Sheba Medical Center, Israel between 2014 and 2023, were used. Samples were stained with PAS and GMS and digitized by the Philips IntelliSite scanner. DeePathology® STUDIO fungal elements were annotated and deemed as ground truth data after an overall revision by two specialist pathologists. Subsequently, they were used to create an AI-based solution, which has been further validated in other regions of interests. The study participants were divided into two cohorts. In the first cohort, the overall sensitivity of the algorithm was 0.8, specificity 0.97, F1 score 0.78; in the second, the overall sensitivity of the algorithm was 0.93, specificity 0.99, F1 score 0.95. The results obtained are encouraging as proof of concept for an AI-based fungi detection algorithm. DeePathology® STUDIO can be employed as a decision support system for pathologists when diagnosing a cutaneous fungal infection using PAS and GMS stains, thereby, saving time and money.
AB - Cutaneous fungal infections are one of the most common skin conditions, hence, the burden of determining fungal elements upon microscopic examination with periodic acid-Schiff (PAS) and Gomori methenamine silver (GMS) stains, is very time consuming. Despite some morphological variability posing challenges to training artificial intelligence (AI)-based solutions, these structures are favored potential targets, enabling the recruitment of promising AI-based technologies. Herein, we present a novel AI solution for identifying skin fungal infections, potentially providing a decision support system for pathologists. Skin biopsies of patients diagnosed with a cutaneous fungal infection at the Sheba Medical Center, Israel between 2014 and 2023, were used. Samples were stained with PAS and GMS and digitized by the Philips IntelliSite scanner. DeePathology® STUDIO fungal elements were annotated and deemed as ground truth data after an overall revision by two specialist pathologists. Subsequently, they were used to create an AI-based solution, which has been further validated in other regions of interests. The study participants were divided into two cohorts. In the first cohort, the overall sensitivity of the algorithm was 0.8, specificity 0.97, F1 score 0.78; in the second, the overall sensitivity of the algorithm was 0.93, specificity 0.99, F1 score 0.95. The results obtained are encouraging as proof of concept for an AI-based fungi detection algorithm. DeePathology® STUDIO can be employed as a decision support system for pathologists when diagnosing a cutaneous fungal infection using PAS and GMS stains, thereby, saving time and money.
KW - AI (artificial intelligence)
KW - DeePathology
KW - Dermatopathology
KW - Fungal elements
KW - Fungi
UR - http://www.scopus.com/inward/record.url?scp=85199998961&partnerID=8YFLogxK
U2 - 10.1016/j.prp.2024.155480
DO - 10.1016/j.prp.2024.155480
M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???
C2 - 39088874
AN - SCOPUS:85199998961
SN - 0344-0338
VL - 261
JO - Pathology Research and Practice
JF - Pathology Research and Practice
M1 - 155480
ER -