: 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.

Artificial intelligence-based tools applied to pathological diagnosis of microbiological diseases / Marletta, Stefano; L'Imperio, Vincenzo; Eccher, Albino; Antonini, Pietro; Santonicco, Nicola; Girolami, Ilaria; Tos, Angelo Paolo Dei; Sbaraglia, Marta; Pagni, Fabio; Brunelli, Matteo; Marino, Andrea; Scarpa, Aldo; Munari, Enrico; Fusco, Nicola; Pantanowitz, Liron. - In: PATHOLOGY RESEARCH AND PRACTICE. - ISSN 0344-0338. - 243:(2023), pp. 1-6. [10.1016/j.prp.2023.154362]

Artificial intelligence-based tools applied to pathological diagnosis of microbiological diseases

Eccher, Albino;
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.
2023
243
1
6
Artificial intelligence-based tools applied to pathological diagnosis of microbiological diseases / Marletta, Stefano; L'Imperio, Vincenzo; Eccher, Albino; Antonini, Pietro; Santonicco, Nicola; Girolami, Ilaria; Tos, Angelo Paolo Dei; Sbaraglia, Marta; Pagni, Fabio; Brunelli, Matteo; Marino, Andrea; Scarpa, Aldo; Munari, Enrico; Fusco, Nicola; Pantanowitz, Liron. - In: PATHOLOGY RESEARCH AND PRACTICE. - ISSN 0344-0338. - 243:(2023), pp. 1-6. [10.1016/j.prp.2023.154362]
Marletta, Stefano; L'Imperio, Vincenzo; Eccher, Albino; Antonini, Pietro; Santonicco, Nicola; Girolami, Ilaria; Tos, Angelo Paolo Dei; Sbaraglia, Marta; Pagni, Fabio; Brunelli, Matteo; Marino, Andrea; Scarpa, Aldo; Munari, Enrico; Fusco, Nicola; Pantanowitz, Liron
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1317461
Citazioni
  • ???jsp.display-item.citation.pmc??? 1
  • Scopus 11
  • ???jsp.display-item.citation.isi??? 7
social impact