A web-based artificial intelligence system for label-free virus classification and detection of cytopathic effects
Abstract Identifying viral replication within cells demands labor-intensive isolation methods, requiring specialized personnel and additional confirmatory tests. To facilitate this process, we developed an AI-powered automated system called AI Recognition of Viral CPE (AIRVIC), specifically designed...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-89639-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849715768822857728 |
|---|---|
| author | Zeynep Akkutay-Yoldar Mehmet Türkay Yoldar Yiğit Burak Akkaş Sibel Şurak Furkan Garip Oğuzcan Turan Bengisu Ekizoğlu Osman Can Yüca Aykut Özkul Barış Ünver |
| author_facet | Zeynep Akkutay-Yoldar Mehmet Türkay Yoldar Yiğit Burak Akkaş Sibel Şurak Furkan Garip Oğuzcan Turan Bengisu Ekizoğlu Osman Can Yüca Aykut Özkul Barış Ünver |
| author_sort | Zeynep Akkutay-Yoldar |
| collection | DOAJ |
| description | Abstract Identifying viral replication within cells demands labor-intensive isolation methods, requiring specialized personnel and additional confirmatory tests. To facilitate this process, we developed an AI-powered automated system called AI Recognition of Viral CPE (AIRVIC), specifically designed to detect and classify label-free cytopathic effects (CPEs) induced by SARS-CoV-2, BAdV-1, BPIV3, BoAHV-1, and two strains of BoGHV-4 in Vero and MDBK cell lines. AIRVIC utilizes convolutional neural networks, with ResNet50 as the primary architecture, trained on 40,369 microscopy images at various magnifications. AIRVIC demonstrated strong CPE detection, achieving 100% accuracy for the BoGHV-4 DN-599 strain in MDBK cells, the highest among tested strains. In contrast, the BoGHV-4 MOVAR 33/63 strain in Vero cells showed a lower accuracy of 87.99%, the lowest among all models tested. For virus classification, a multi-class accuracy of 87.61% was achieved for bovine viruses in MDBK cells; however, it dropped to 63.44% when the virus was identified without specifying the cell line. To the best of our knowledge, this is the first research article published in English to utilize AI for distinguishing animal virus infections in cell culture. AIRVIC’s hierarchical structure highlights its adaptability to virological diagnostics, providing unbiased infectivity scoring and facilitating viral isolation and antiviral efficacy testing. Additionally, AIRVIC is accessible as a web-based platform, allowing global researchers to leverage its capabilities in viral diagnostics and beyond. |
| format | Article |
| id | doaj-art-004e3dbdbc404c2f8caab264f07071ae |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-004e3dbdbc404c2f8caab264f07071ae2025-08-20T03:13:12ZengNature PortfolioScientific Reports2045-23222025-02-0115111510.1038/s41598-025-89639-0A web-based artificial intelligence system for label-free virus classification and detection of cytopathic effectsZeynep Akkutay-Yoldar0Mehmet Türkay Yoldar1Yiğit Burak Akkaş2Sibel Şurak3Furkan Garip4Oğuzcan Turan5Bengisu Ekizoğlu6Osman Can Yüca7Aykut Özkul8Barış Ünver9Department of Virology, Faculty of Veterinary Medicine, Ankara UniversityTURK AI Artificial Intelligence Information and Software Systems, Bilkent CyberparkTURK AI Artificial Intelligence Information and Software Systems, Bilkent CyberparkGraduate School of Health Sciences, Ankara UniversityGraduate School of Health Sciences, Ankara UniversityTURK AI Artificial Intelligence Information and Software Systems, Bilkent CyberparkTURK AI Artificial Intelligence Information and Software Systems, Bilkent CyberparkTURK AI Artificial Intelligence Information and Software Systems, Bilkent CyberparkDepartment of Virology, Faculty of Veterinary Medicine, Ankara UniversityTURK AI Artificial Intelligence Information and Software Systems, Bilkent CyberparkAbstract Identifying viral replication within cells demands labor-intensive isolation methods, requiring specialized personnel and additional confirmatory tests. To facilitate this process, we developed an AI-powered automated system called AI Recognition of Viral CPE (AIRVIC), specifically designed to detect and classify label-free cytopathic effects (CPEs) induced by SARS-CoV-2, BAdV-1, BPIV3, BoAHV-1, and two strains of BoGHV-4 in Vero and MDBK cell lines. AIRVIC utilizes convolutional neural networks, with ResNet50 as the primary architecture, trained on 40,369 microscopy images at various magnifications. AIRVIC demonstrated strong CPE detection, achieving 100% accuracy for the BoGHV-4 DN-599 strain in MDBK cells, the highest among tested strains. In contrast, the BoGHV-4 MOVAR 33/63 strain in Vero cells showed a lower accuracy of 87.99%, the lowest among all models tested. For virus classification, a multi-class accuracy of 87.61% was achieved for bovine viruses in MDBK cells; however, it dropped to 63.44% when the virus was identified without specifying the cell line. To the best of our knowledge, this is the first research article published in English to utilize AI for distinguishing animal virus infections in cell culture. AIRVIC’s hierarchical structure highlights its adaptability to virological diagnostics, providing unbiased infectivity scoring and facilitating viral isolation and antiviral efficacy testing. Additionally, AIRVIC is accessible as a web-based platform, allowing global researchers to leverage its capabilities in viral diagnostics and beyond.https://doi.org/10.1038/s41598-025-89639-0BoAHV-1BoGHV-4BPIV3BAdV-1CPEDeep learning |
| spellingShingle | Zeynep Akkutay-Yoldar Mehmet Türkay Yoldar Yiğit Burak Akkaş Sibel Şurak Furkan Garip Oğuzcan Turan Bengisu Ekizoğlu Osman Can Yüca Aykut Özkul Barış Ünver A web-based artificial intelligence system for label-free virus classification and detection of cytopathic effects Scientific Reports BoAHV-1 BoGHV-4 BPIV3 BAdV-1 CPE Deep learning |
| title | A web-based artificial intelligence system for label-free virus classification and detection of cytopathic effects |
| title_full | A web-based artificial intelligence system for label-free virus classification and detection of cytopathic effects |
| title_fullStr | A web-based artificial intelligence system for label-free virus classification and detection of cytopathic effects |
| title_full_unstemmed | A web-based artificial intelligence system for label-free virus classification and detection of cytopathic effects |
| title_short | A web-based artificial intelligence system for label-free virus classification and detection of cytopathic effects |
| title_sort | web based artificial intelligence system for label free virus classification and detection of cytopathic effects |
| topic | BoAHV-1 BoGHV-4 BPIV3 BAdV-1 CPE Deep learning |
| url | https://doi.org/10.1038/s41598-025-89639-0 |
| work_keys_str_mv | AT zeynepakkutayyoldar awebbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects AT mehmetturkayyoldar awebbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects AT yigitburakakkas awebbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects AT sibelsurak awebbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects AT furkangarip awebbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects AT oguzcanturan awebbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects AT bengisuekizoglu awebbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects AT osmancanyuca awebbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects AT aykutozkul awebbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects AT barısunver awebbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects AT zeynepakkutayyoldar webbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects AT mehmetturkayyoldar webbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects AT yigitburakakkas webbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects AT sibelsurak webbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects AT furkangarip webbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects AT oguzcanturan webbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects AT bengisuekizoglu webbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects AT osmancanyuca webbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects AT aykutozkul webbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects AT barısunver webbasedartificialintelligencesystemforlabelfreevirusclassificationanddetectionofcytopathiceffects |