Artificial intelligence-based tools applied to pathological diagnosis of microbiological diseases
Open accessAbstract
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.
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There is potential for using AI models for the detection of fungal organisms, as already reported in other fields of histopathology.
“Hence, there is potential for using AI models for the detection of fungal organisms, as already reported in other fields of histopathology [27,28,29,30,31].”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [28]
Dependence on special stains meant there was less potential of shortening turnaround times and reducing the use of resources.
“Additionally, the dependence on special stains meant there was less potential of shortening turnaround times and reducing the use of resources [26,27,28,29,30,31,32].”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [28]
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- Authors
- Marletta, S., L’Imperio, V., Eccher, A., Antonini, P., Santonicco, N., Girolami, I., Dei Tos, A. P., Sbaraglia, M., Pagni, F., Brunelli, M., Marino, A., Scarpa, A., Munari, E., Fusco, N., & Pantanowitz, L.
- Affiliations
- University of Verona (IT)
- Azienda Ospedaliera San Gerardo (IT)
- Ospedale di Bolzano (IT)
- University of Padua (IT)
- University of Brescia (IT)
- European Institute of Oncology (IT)
- University of Michigan (US)
- Corresponding author
- Eccher, Albino
- ORCID
- Venue
- Pathology - Research and Practice
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 243
- Pages
- 154362
- ISSN
- 0344-0338 (ISSN-L)
- OpenAlex
- W4319320194
- Cited by
- 49
- References
- 35
- Citations by year
- 2026: 6 · 2025: 18 · 2024: 17 · 2023: 8
- FWCI
- 1.03
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Pathology - Research and Practice (publishedVersion)
- PubMed (publishedVersion)
- BOA (University of Milano-Bicocca) (submittedVersion)
- Institutional Research Information System (Università degli Studi di Brescia) (submittedVersion)
- IRIS UNIMORE (University of Modena and Reggio Emilia) (submittedVersion, cc-by)
- Padua Research Archive (University of Padova) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Keywords
- artificial intelligence, microbiological diagnosis, infectious diseases, malaria, bacterial identification, nematode detection, protozoa, cytology specimens, deep learning, digital microscopy images, resource-limited settings
- MeSH terms
- Pathologists, Algorithms, Artificial Intelligence, Humans, Microscopy, Databases, Factual
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