Development of Automated Image Processing for High-Throughput Screening of Potential Anti-Chikungunya Virus Compounds
Chikungunya virus, a member of the Alphavirus genus, continues to present a global health challenge due to its widespread occurrence and the absence of specific antiviral therapies. Accurate detection of viral infections, such as chikungunya, is critical for antiviral research, yet traditional metho...
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2025-01-01
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| author | Pathaphon Wiriwithya Siwaporn Boonyasuppayakorn Pattadon Sawetpiyakul Duangpron Peypala Gridsada Phanomchoeng |
| author_facet | Pathaphon Wiriwithya Siwaporn Boonyasuppayakorn Pattadon Sawetpiyakul Duangpron Peypala Gridsada Phanomchoeng |
| author_sort | Pathaphon Wiriwithya |
| collection | DOAJ |
| description | Chikungunya virus, a member of the Alphavirus genus, continues to present a global health challenge due to its widespread occurrence and the absence of specific antiviral therapies. Accurate detection of viral infections, such as chikungunya, is critical for antiviral research, yet traditional methods are time-consuming and prone to error. This study presents the development and validation of an automated image processing algorithm designed to improve the accuracy and speed of high-throughput screening for potential anti-chikungunya virus compounds. Using MvTec Halcon software (Version 22.11), the algorithm was developed to detect and classify infected and uninfected cells in viral assays, and its performance was validated against manual counts conducted by virology experts, showing a strong correlation with Pearson correlation coefficients of 0.9807 for cell detection and 0.9886 for virus detection. These values indicate a high correlation between the algorithm and manual counts performed by three virology experts, demonstrating that the algorithm’s accuracy closely matches expert manual evaluations. Following statistical validation, the algorithm was applied to screen antiviral compounds, demonstrating its effectiveness in enhancing the throughput and accuracy of drug discovery workflows. This technology can be seamlessly integrated into existing virological research pipelines, offering a scalable and efficient tool to accelerate drug discovery and improve diagnostic workflows for vector-borne and emerging viral diseases. By addressing critical bottlenecks in speed and accuracy, it holds promise for tackling global virology challenges and advancing research into other viral infections. |
| format | Article |
| id | doaj-art-a01a3f238fff429e924087704ecbde80 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a01a3f238fff429e924087704ecbde802025-01-10T13:15:22ZengMDPI AGApplied Sciences2076-34172025-01-0115138510.3390/app15010385Development of Automated Image Processing for High-Throughput Screening of Potential Anti-Chikungunya Virus CompoundsPathaphon Wiriwithya0Siwaporn Boonyasuppayakorn1Pattadon Sawetpiyakul2Duangpron Peypala3Gridsada Phanomchoeng4Department of Mechanical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, ThailandDepartment of Microbiology, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, ThailandDepartment of Biology, Faculty of Science, Chulalongkorn University, Bangkok 10330, ThailandDepartment of Microbiology, Faculty of Science, Chulalongkorn University, Bangkok 10330, ThailandDepartment of Mechanical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, ThailandChikungunya virus, a member of the Alphavirus genus, continues to present a global health challenge due to its widespread occurrence and the absence of specific antiviral therapies. Accurate detection of viral infections, such as chikungunya, is critical for antiviral research, yet traditional methods are time-consuming and prone to error. This study presents the development and validation of an automated image processing algorithm designed to improve the accuracy and speed of high-throughput screening for potential anti-chikungunya virus compounds. Using MvTec Halcon software (Version 22.11), the algorithm was developed to detect and classify infected and uninfected cells in viral assays, and its performance was validated against manual counts conducted by virology experts, showing a strong correlation with Pearson correlation coefficients of 0.9807 for cell detection and 0.9886 for virus detection. These values indicate a high correlation between the algorithm and manual counts performed by three virology experts, demonstrating that the algorithm’s accuracy closely matches expert manual evaluations. Following statistical validation, the algorithm was applied to screen antiviral compounds, demonstrating its effectiveness in enhancing the throughput and accuracy of drug discovery workflows. This technology can be seamlessly integrated into existing virological research pipelines, offering a scalable and efficient tool to accelerate drug discovery and improve diagnostic workflows for vector-borne and emerging viral diseases. By addressing critical bottlenecks in speed and accuracy, it holds promise for tackling global virology challenges and advancing research into other viral infections.https://www.mdpi.com/2076-3417/15/1/385automated image processinghigh-throughput screeningbiomedical imagingimage analysis in virologyimage analysis |
| spellingShingle | Pathaphon Wiriwithya Siwaporn Boonyasuppayakorn Pattadon Sawetpiyakul Duangpron Peypala Gridsada Phanomchoeng Development of Automated Image Processing for High-Throughput Screening of Potential Anti-Chikungunya Virus Compounds Applied Sciences automated image processing high-throughput screening biomedical imaging image analysis in virology image analysis |
| title | Development of Automated Image Processing for High-Throughput Screening of Potential Anti-Chikungunya Virus Compounds |
| title_full | Development of Automated Image Processing for High-Throughput Screening of Potential Anti-Chikungunya Virus Compounds |
| title_fullStr | Development of Automated Image Processing for High-Throughput Screening of Potential Anti-Chikungunya Virus Compounds |
| title_full_unstemmed | Development of Automated Image Processing for High-Throughput Screening of Potential Anti-Chikungunya Virus Compounds |
| title_short | Development of Automated Image Processing for High-Throughput Screening of Potential Anti-Chikungunya Virus Compounds |
| title_sort | development of automated image processing for high throughput screening of potential anti chikungunya virus compounds |
| topic | automated image processing high-throughput screening biomedical imaging image analysis in virology image analysis |
| url | https://www.mdpi.com/2076-3417/15/1/385 |
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