Back to Search
Start Over
Artificial intelligence-based tools applied to pathological diagnosis of microbiological diseases.
- Source :
-
Pathology, research and practice [Pathol Res Pract] 2023 Mar; Vol. 243, pp. 154362. Date of Electronic Publication: 2023 Feb 06. - Publication Year :
- 2023
-
Abstract
- Infectious diseases still threaten the global community, especially in resource-limited countries. An accurate diagnosis is paramount to proper patient and public health management. Identification of many microbes still relies on manual microscopic examination, a time-consuming process requiring skilled staff. Thus, artificial intelligence (AI) has been exploited for identification of microorganisms. A systematic search was carried out using electronic databases looking for studies dealing with the application of AI to pathology microbiology specimens. Of 4596 retrieved articles, 110 were included. The main applications of AI regarded malaria (54 studies), bacteria (28), nematodes (14), and other protozoa (11). Most publications examined cytological material (95, 86%), mainly analyzing images acquired through microscope cameras (65, 59%) or coupled with smartphones (16, 15%). Various deep-learning strategies were used for the analysis of digital images, achieving highly satisfactory results. The published evidence suggests that AI can be reliably utilized for assisting pathologists in the detection of microorganisms. Further technologic improvement and availability of datasets for training AI-based algorithms would help expand this field and widen its adoption, especially for developing countries.<br />Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2023 Elsevier GmbH. All rights reserved.)
- Subjects :
- Humans
Databases, Factual
Microscopy
Pathologists
Artificial Intelligence
Algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 1618-0631
- Volume :
- 243
- Database :
- MEDLINE
- Journal :
- Pathology, research and practice
- Publication Type :
- Academic Journal
- Accession number :
- 36758417
- Full Text :
- https://doi.org/10.1016/j.prp.2023.154362