Publicação
Artificial intelligence-powered microscopy: transforming the landscape of parasitology
| dc.contributor.author | Niz, Mariana De | |
| dc.contributor.author | Pereira, Sara Silva | |
| dc.contributor.author | Kirchenbuechler, David | |
| dc.contributor.author | Lemgruber, Leandro | |
| dc.contributor.author | Arvanitis, Constadina | |
| dc.date.accessioned | 2025-07-16T07:26:24Z | |
| dc.date.available | 2025-07-16T07:26:24Z | |
| dc.date.issued | 2026-02-01 | |
| dc.description.abstract | Microscopy and image analysis play a vital role in parasitology research; they are critical for identifying parasitic organisms and elucidating their complex life cycles. Despite major advancements in imaging and analysis, several challenges remain. These include the integration of interdisciplinary data; information derived from various model organisms; and data acquired from clinical research. In our view, artificial intelligence—with the latest advances in machine and deep learning—holds enormous potential to address many of these challenges. This review addresses how artificial intelligence, machine learning and deep learning have been used in the field of parasitology—mainly focused on Apicomplexan, Diplomonad, and Kinetoplastid groups. We explore how gaps in our understanding could be filled by AI in future parasitology research and diagnosis in the field. Moreover, it addresses challenges and limitations currently faced in implementing and expanding the use of artificial intelligence across biomedical fields. The necessary increased collaboration between biologists and computational scientists will facilitate understanding, development, and implementation of the latest advances for both scientific discovery and clinical impact. Current and future AI tools hold the potential to revolutionise parasitology and expand One Health principles. | eng |
| dc.identifier.doi | 10.1111/jmi.13433 | |
| dc.identifier.eid | 105007789656 | |
| dc.identifier.issn | 0022-2720 | |
| dc.identifier.pmid | 40492595 | |
| dc.identifier.uri | http://hdl.handle.net/10400.14/53912 | |
| dc.identifier.wos | 001505157800001 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | AI-based diagnosis | |
| dc.subject | Deep learning | |
| dc.subject | Host-pathogen interactions | |
| dc.subject | Image analysis | |
| dc.subject | Microscopy | |
| dc.subject | Parasitology | |
| dc.title | Artificial intelligence-powered microscopy: transforming the landscape of parasitology | eng |
| dc.type | review article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 329 | |
| oaire.citation.issue | 2 | |
| oaire.citation.startPage | 280 | |
| oaire.citation.title | Journal of Microscopy | |
| oaire.citation.volume | 301 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 |