AI-Powered Mobile App for Nuclear Cataract Detection
Cataract remains the leading cause of blindness worldwide, and the number of individuals affected by this condition is expected to rise significantly due to global population ageing. Early diagnosis is crucial, as delayed treatment may result in irreversible vision loss. This study explores and pres...
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/13/3954 |
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| author | Alicja Anna Ignatowicz Tomasz Marciniak Elżbieta Marciniak |
| author_facet | Alicja Anna Ignatowicz Tomasz Marciniak Elżbieta Marciniak |
| author_sort | Alicja Anna Ignatowicz |
| collection | DOAJ |
| description | Cataract remains the leading cause of blindness worldwide, and the number of individuals affected by this condition is expected to rise significantly due to global population ageing. Early diagnosis is crucial, as delayed treatment may result in irreversible vision loss. This study explores and presents a mobile application for Android devices designed for the detection of cataracts using deep learning models. The proposed solution utilizes a multi-stage classification approach to analyze ocular images acquired with a slit lamp, sourced from the Nuclear Cataract Database for Biomedical and Machine Learning Applications. The process involves identifying pathological features and assessing the severity of the detected condition, enabling comprehensive characterization of the NC (nuclear cataract) of cataract progression based on the LOCS III scale classification. The evaluation included a range of convolutional neural network architectures, from larger models like VGG16 and ResNet50, to lighter alternatives such as VGG11, ResNet18, MobileNetV2, and EfficientNet-B0. All models demonstrated comparable performance, with classification accuracies exceeding 91–94.5%. The trained models were optimized for mobile deployment, enabling real-time analysis of eye images captured with the device camera or selected from local storage. The presented mobile application, trained and validated on authentic clinician-labeled pictures, represents a significant advancement over existing mobile tools. The preliminary evaluations demonstrated a high accuracy in cataract detection and severity grading. These results confirm the approach is feasible and will serve as the foundation for ongoing development and extensions. |
| format | Article |
| id | doaj-art-bba00ffeea204e19b8219c42a30a02bf |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-bba00ffeea204e19b8219c42a30a02bf2025-08-20T02:36:31ZengMDPI AGSensors1424-82202025-06-012513395410.3390/s25133954AI-Powered Mobile App for Nuclear Cataract DetectionAlicja Anna Ignatowicz0Tomasz Marciniak1Elżbieta Marciniak2Division of Electronic Systems and Signal Processing, Institute of Automatic Control and Robotics, Poznan University of Technology, 60-965 Poznan, PolandDivision of Electronic Systems and Signal Processing, Institute of Automatic Control and Robotics, Poznan University of Technology, 60-965 Poznan, PolandDepartment of Ophthalmology, Chair of Ophthalmology and Optometry, Heliodor Swiecicki University Hospital, Poznan University of Medical Sciences, 60-780 Poznan, PolandCataract remains the leading cause of blindness worldwide, and the number of individuals affected by this condition is expected to rise significantly due to global population ageing. Early diagnosis is crucial, as delayed treatment may result in irreversible vision loss. This study explores and presents a mobile application for Android devices designed for the detection of cataracts using deep learning models. The proposed solution utilizes a multi-stage classification approach to analyze ocular images acquired with a slit lamp, sourced from the Nuclear Cataract Database for Biomedical and Machine Learning Applications. The process involves identifying pathological features and assessing the severity of the detected condition, enabling comprehensive characterization of the NC (nuclear cataract) of cataract progression based on the LOCS III scale classification. The evaluation included a range of convolutional neural network architectures, from larger models like VGG16 and ResNet50, to lighter alternatives such as VGG11, ResNet18, MobileNetV2, and EfficientNet-B0. All models demonstrated comparable performance, with classification accuracies exceeding 91–94.5%. The trained models were optimized for mobile deployment, enabling real-time analysis of eye images captured with the device camera or selected from local storage. The presented mobile application, trained and validated on authentic clinician-labeled pictures, represents a significant advancement over existing mobile tools. The preliminary evaluations demonstrated a high accuracy in cataract detection and severity grading. These results confirm the approach is feasible and will serve as the foundation for ongoing development and extensions.https://www.mdpi.com/1424-8220/25/13/3954cataractneural networkssmartphone appAndroid |
| spellingShingle | Alicja Anna Ignatowicz Tomasz Marciniak Elżbieta Marciniak AI-Powered Mobile App for Nuclear Cataract Detection Sensors cataract neural networks smartphone app Android |
| title | AI-Powered Mobile App for Nuclear Cataract Detection |
| title_full | AI-Powered Mobile App for Nuclear Cataract Detection |
| title_fullStr | AI-Powered Mobile App for Nuclear Cataract Detection |
| title_full_unstemmed | AI-Powered Mobile App for Nuclear Cataract Detection |
| title_short | AI-Powered Mobile App for Nuclear Cataract Detection |
| title_sort | ai powered mobile app for nuclear cataract detection |
| topic | cataract neural networks smartphone app Android |
| url | https://www.mdpi.com/1424-8220/25/13/3954 |
| work_keys_str_mv | AT alicjaannaignatowicz aipoweredmobileappfornuclearcataractdetection AT tomaszmarciniak aipoweredmobileappfornuclearcataractdetection AT elzbietamarciniak aipoweredmobileappfornuclearcataractdetection |