A publicly available pharyngitis dataset and baseline evaluations for bacterial or nonbacterial classification
Abstract Accurate and early differentiation between bacterial and nonbacterial pharyngitis is crucial for optimizing treatment and minimizing unnecessary antibiotic use. The similar clinical presentation of sore throat in bacterial and nonbacterial infections poses significant diagnostic challenges,...
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| Format: | Article |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05780-5 |
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| author | Negar Shojaei Habib Rostami Mohammad Barzegar Shokooh Saadat Farzaneh Zohreh Farrar Majid Alimohammadi Jahanbakhsh Keyvani Mehdi Mirzad Leila Gonbadi |
| author_facet | Negar Shojaei Habib Rostami Mohammad Barzegar Shokooh Saadat Farzaneh Zohreh Farrar Majid Alimohammadi Jahanbakhsh Keyvani Mehdi Mirzad Leila Gonbadi |
| author_sort | Negar Shojaei |
| collection | DOAJ |
| description | Abstract Accurate and early differentiation between bacterial and nonbacterial pharyngitis is crucial for optimizing treatment and minimizing unnecessary antibiotic use. The similar clinical presentation of sore throat in bacterial and nonbacterial infections poses significant diagnostic challenges, even for experienced clinicians. To address this, we developed a publicly available dataset consisting of high-resolution throat images captured using smartphone cameras. These images were analyzed through deep neural networks to distinguish between bacterial and nonbacterial infections based on visual features and symptoms. The dataset is the largest publicly available dataset in this field, which includes images from 742 patients experiencing common cold symptoms. For each patient, it also records the presence or absence of 20 symptoms, age, gender, and between 4 to 9 diagnoses by different physicians. Furthermore, three baseline models were established to differentiate bacterial from nonbacterial infections. Our goal is to enhance the field of non-invasive and accurate pharyngitis diagnosis, drive the development of AI-driven diagnostic tools, promote remote healthcare solutions, and inspire future innovations in medical image analysis. |
| format | Article |
| id | doaj-art-1fc0dd0a2886474abe9f7bf44d03cdac |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-1fc0dd0a2886474abe9f7bf44d03cdac2025-08-20T03:42:34ZengNature PortfolioScientific Data2052-44632025-08-011211810.1038/s41597-025-05780-5A publicly available pharyngitis dataset and baseline evaluations for bacterial or nonbacterial classificationNegar Shojaei0Habib Rostami1Mohammad Barzegar2Shokooh Saadat Farzaneh3Zohreh Farrar4Majid Alimohammadi5Jahanbakhsh Keyvani6Mehdi Mirzad7Leila Gonbadi8Department of Computer Engineering, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf UniversityDepartment of Computer Engineering, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf UniversityDepartment of Computer Engineering, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf UniversityDepartment of Medicine, Sina Hospital of Junqan, Shahrekord University of Medical Sciences, Chaharmahal and BakhtiariThe Persian Gulf Marine Biotechnology Research Center, The Persian Gulf Biomedical Sciences Research Institute, Bushehr University of Medical SciencesDepartment of Medicine, Bushehr University of Medical SciencesDepartment of Medicine, Shahrekord University of Medical Sciences, Chaharmahal and BakhtiariFaculty of Management and Economics, Islamic Azad University, Science and Research BranchDepartment of Computer Engineering, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf UniversityAbstract Accurate and early differentiation between bacterial and nonbacterial pharyngitis is crucial for optimizing treatment and minimizing unnecessary antibiotic use. The similar clinical presentation of sore throat in bacterial and nonbacterial infections poses significant diagnostic challenges, even for experienced clinicians. To address this, we developed a publicly available dataset consisting of high-resolution throat images captured using smartphone cameras. These images were analyzed through deep neural networks to distinguish between bacterial and nonbacterial infections based on visual features and symptoms. The dataset is the largest publicly available dataset in this field, which includes images from 742 patients experiencing common cold symptoms. For each patient, it also records the presence or absence of 20 symptoms, age, gender, and between 4 to 9 diagnoses by different physicians. Furthermore, three baseline models were established to differentiate bacterial from nonbacterial infections. Our goal is to enhance the field of non-invasive and accurate pharyngitis diagnosis, drive the development of AI-driven diagnostic tools, promote remote healthcare solutions, and inspire future innovations in medical image analysis.https://doi.org/10.1038/s41597-025-05780-5 |
| spellingShingle | Negar Shojaei Habib Rostami Mohammad Barzegar Shokooh Saadat Farzaneh Zohreh Farrar Majid Alimohammadi Jahanbakhsh Keyvani Mehdi Mirzad Leila Gonbadi A publicly available pharyngitis dataset and baseline evaluations for bacterial or nonbacterial classification Scientific Data |
| title | A publicly available pharyngitis dataset and baseline evaluations for bacterial or nonbacterial classification |
| title_full | A publicly available pharyngitis dataset and baseline evaluations for bacterial or nonbacterial classification |
| title_fullStr | A publicly available pharyngitis dataset and baseline evaluations for bacterial or nonbacterial classification |
| title_full_unstemmed | A publicly available pharyngitis dataset and baseline evaluations for bacterial or nonbacterial classification |
| title_short | A publicly available pharyngitis dataset and baseline evaluations for bacterial or nonbacterial classification |
| title_sort | publicly available pharyngitis dataset and baseline evaluations for bacterial or nonbacterial classification |
| url | https://doi.org/10.1038/s41597-025-05780-5 |
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