Next-Generation Teleophthalmology: AI-enabled Quality Assessment Aiding Remote Smartphone-based Consultation
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911979871928320 |
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| author | Srikanth, Dhruv Gurung, Jayang Deepika, N Satya Joshi, Vineet Giri, Lopamudra Vaddavalli, Pravin Jana, Soumya |
| author_facet | Srikanth, Dhruv Gurung, Jayang Deepika, N Satya Joshi, Vineet Giri, Lopamudra Vaddavalli, Pravin Jana, Soumya |
| contents | Blindness and other eye diseases are a global health concern, particularly in low- and middle-income countries like India. In this regard, during the COVID-19 pandemic, teleophthalmology became a lifeline, and the Grabi attachment for smartphone-based eye imaging gained in use. However, quality of user-captured image often remained inadequate, requiring clinician vetting and delays. In this backdrop, we propose an AI-based quality assessment system with instant feedback mimicking clinicians' judgments and tested on patient-captured images. Dividing the complex problem hierarchically, here we tackle a nontrivial part, and demonstrate a proof of the concept. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07118 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Next-Generation Teleophthalmology: AI-enabled Quality Assessment Aiding Remote Smartphone-based Consultation Srikanth, Dhruv Gurung, Jayang Deepika, N Satya Joshi, Vineet Giri, Lopamudra Vaddavalli, Pravin Jana, Soumya Human-Computer Interaction Artificial Intelligence Machine Learning Image and Video Processing Signal Processing Blindness and other eye diseases are a global health concern, particularly in low- and middle-income countries like India. In this regard, during the COVID-19 pandemic, teleophthalmology became a lifeline, and the Grabi attachment for smartphone-based eye imaging gained in use. However, quality of user-captured image often remained inadequate, requiring clinician vetting and delays. In this backdrop, we propose an AI-based quality assessment system with instant feedback mimicking clinicians' judgments and tested on patient-captured images. Dividing the complex problem hierarchically, here we tackle a nontrivial part, and demonstrate a proof of the concept. |
| title | Next-Generation Teleophthalmology: AI-enabled Quality Assessment Aiding Remote Smartphone-based Consultation |
| topic | Human-Computer Interaction Artificial Intelligence Machine Learning Image and Video Processing Signal Processing |
| url | https://arxiv.org/abs/2402.07118 |