{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T22:32:47Z","timestamp":1793313167260,"version":"4.5.0"},"reference-count":38,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T00:00:00Z","timestamp":1750809600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004239","name":"Poznan University of Technology","doi-asserted-by":"publisher","award":["0211\/SBAD\/0225"],"award-info":[{"award-number":["0211\/SBAD\/0225"]}],"id":[{"id":"10.13039\/501100004239","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u201394.5%. 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.<\/jats:p>","DOI":"10.3390\/s25133954","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T08:23:22Z","timestamp":1750839802000},"page":"3954","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["AI-Powered Mobile App for Nuclear Cataract Detection"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0810-2568","authenticated-orcid":false,"given":"Alicja Anna","family":"Ignatowicz","sequence":"first","affiliation":[{"name":"Division of Electronic Systems and Signal Processing, Institute of Automatic Control and Robotics, Poznan University of Technology, 60-965 Poznan, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6035-7325","authenticated-orcid":false,"given":"Tomasz","family":"Marciniak","sequence":"additional","affiliation":[{"name":"Division of Electronic Systems and Signal Processing, Institute of Automatic Control and Robotics, Poznan University of Technology, 60-965 Poznan, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7009-0406","authenticated-orcid":false,"given":"El\u017cbieta","family":"Marciniak","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Chair of Ophthalmology and Optometry, Heliodor Swiecicki University Hospital, Poznan University of Medical Sciences, 60-780 Poznan, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,25]]},"reference":[{"key":"ref_1","unstructured":"Vision Loss Expert Group of the Global Burden of Disease Study, and GBD 2019 Blindness and Vision Impairment Collaborators (2024). Global estimates on the number of people blind or visually impaired by cataract: A meta-analysis from 2000 to 2020. Eye, 38, 2156\u20132172."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1111\/ceo.13892","article-title":"Cataract and systemic disease: A review","volume":"49","author":"Ang","year":"2021","journal-title":"Clin. Exp. Ophthalmol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1038\/s41433-020-0806-3","article-title":"Global and regional prevalence of age-related cataract: A comprehensive systematic review and meta-analysis","volume":"34","author":"Hashemi","year":"2020","journal-title":"Eye"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2093","DOI":"10.1001\/jama.2025.1597","article-title":"Cataracts: A Review","volume":"333","author":"Chen","year":"2025","journal-title":"JAMA"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"959","DOI":"10.2147\/OPTH.S61372","article-title":"Progression of nuclear sclerosis based on changes in refractive values after lens-sparing vitrectomy in proliferative diabetic retinopathy","volume":"8","author":"Ikeda","year":"2014","journal-title":"Clin. Ophthalmol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"243","DOI":"10.4103\/2008-322X.170356","article-title":"Risk factors for nuclear and cortical cataracts: A hospital-based study","volume":"10","author":"Mamatha","year":"2015","journal-title":"J. Ophthalmic Vis. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1080\/09553002.2020.1812759","article-title":"Etiology of posterior subcapsular cataracts based on a review of risk factors including aging, diabetes, and ionizing radiation","volume":"96","author":"Richardson","year":"2020","journal-title":"Int. J. Radiat. Biol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.1016\/j.jcrs.2004.02.076","article-title":"Morphology of and visual performance with posterior subcapsular cataract","volume":"30","author":"Vasavada","year":"2004","journal-title":"J. Cataract Refract. Surg."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.4103\/ijo.IJO_1023_17","article-title":"Pediatric cataract","volume":"65","author":"Khokhar","year":"2017","journal-title":"Indian J. Ophthalmol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1038\/s41746-020-00329-9","article-title":"Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection","volume":"3","author":"Li","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.jtos.2022.04.005","article-title":"DryEyeRhythm: A reliable and valid smartphone application for the diagnosis assistance of dry eye","volume":"25","author":"Okumura","year":"2022","journal-title":"Ocul. Surf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"155","DOI":"10.4103\/tjo.tjo_7_22","article-title":"Comparison of smartphone application-based visual acuity with traditional visual acuity chart for use in tele-ophthalmology","volume":"12","author":"Bhaskaran","year":"2022","journal-title":"Taiwan J. Ophthalmol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.jpeds.2019.06.021","article-title":"Visual acuity assessment and vision screening using a novel smartphone application","volume":"213","author":"Zhao","year":"2019","journal-title":"J. Pediatr."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e1404","DOI":"10.7717\/peerj.1404","article-title":"A predictive screening tool to detect diabetic retinopathy or macular edema in primary health care: Construction, validation and implementation on a mobile application","volume":"3","author":"Azrak","year":"2015","journal-title":"PeerJ"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100342","DOI":"10.1016\/j.xops.2023.100342","article-title":"Clinical Utility of Smartphone Applications in Ophthalmology: A Systematic Review","volume":"4","author":"Nagino","year":"2024","journal-title":"Ophthalmol. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"229","DOI":"10.3928\/01913913-20190516-01","article-title":"Evaluation of a free public smartphone application to detect leukocoria in high-risk children aged 1 to 6 Years","volume":"56","author":"Vagge","year":"2019","journal-title":"J. Pediatr. Ophthalmol. Strabismus"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kaur, M., Kaur, J., and Kaur, R. (2015, January 8\u201310). Low cost cataract detection system using smart phone. Proceedings of the 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), Greater Noida, India.","DOI":"10.1109\/ICGCIoT.2015.7380724"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rana, J., and Galib, S.M. (2017, January 7\u20139). Cataract detection using smartphone. Proceedings of the 2017 3rd International Conference on Electrical Information and Communication Technology (EICT), Khulna, Bangladesh.","DOI":"10.1109\/EICT.2017.8275136"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"174169","DOI":"10.1109\/ACCESS.2020.3025346","article-title":"Unified Diagnosis Framework for Automated Nuclear Cataract Grading Based on Smartphone Slit-Lamp Images","volume":"8","author":"Hu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3800110","DOI":"10.1109\/JTEHM.2021.3074597","article-title":"Detecting Cataract Using Smartphones","volume":"9","author":"Askarian","year":"2021","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1049\/cps2.12083","article-title":"Mobile detection of cataracts with an optimised lightweight deep Edge Intelligent technique","volume":"9","author":"Neogi","year":"2024","journal-title":"Iet-Cyber-Phys. Syst. Theory Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1038\/s41597-024-03193-4","article-title":"Cataract-1K Dataset for Deep-Learning-Assisted Analysis of Cataract Surgery Videos","volume":"11","author":"Ghamsarian","year":"2024","journal-title":"Sci. Data"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.media.2018.11.008","article-title":"CATARACTS: Challenge on Automatic Tool Annotation for Cataract Surgery","volume":"52","author":"Lamard","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107754","DOI":"10.1109\/ACCESS.2023.3312616","article-title":"Nuclear Cataract Database for Biomedical and Machine Learning Applications","volume":"11","year":"2023","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1001\/archopht.1993.01090060119035","article-title":"The Lens Opacities Classification System III","volume":"111","author":"Chylack","year":"1993","journal-title":"Arch. Ophthalmol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","article-title":"Support vector data description","volume":"54","author":"Tax","year":"2004","journal-title":"Mach. Learn."},{"key":"ref_27","first-page":"139","article-title":"One-class SVMs for document classification","volume":"2","author":"Manevitz","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref_28","unstructured":"Seeb\u00f6ck, P., Waldstein, S., Klimscha, S., Gerendas, B.S., Donner, R., Schlegl, T., Schmidt-Erfurth, U., and Langs, G. (2016). Identifying and categorizing anomalies in retinal imaging data. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s10278-021-00558-8","article-title":"Unsupervised Deep Anomaly Detection for Medical Images Using an Improved Adversarial Autoencoder","volume":"35","author":"Zhang","year":"2022","journal-title":"J. Digit. Imaging"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7086367","DOI":"10.1155\/2020\/7086367","article-title":"Deep Autoencoders and Feedforward Networks Based on a New Regularization for Anomaly Detection","volume":"2020","author":"Albahar","year":"2020","journal-title":"Secur. Commun. Netw."},{"key":"ref_31","first-page":"148","article-title":"Anomaly Detection on Medical Images Using Autoencoder and Convolutional Neural Network","volume":"12","author":"Siddalingappa","year":"2021","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"852","DOI":"10.35940\/ijrte.C4038.098319","article-title":"A Framework for Medical Data Analysis Using Deep Learning Based on Conventional Neural Network (CNN) and Variational Auto-Encoder","volume":"8","author":"Shabbeer","year":"2019","journal-title":"Int. J. Recent Technol. Eng. (IJRTE)"},{"key":"ref_33","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-Encoding Variational Bayes. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Roy, M., Kong, J., Kashyap, S., Pastore, V.P., Wang, F., Wong, K.C.L., and Mukherjee, V. (2021). Convolutional autoencoder based model HistoCAE for segmentation of viable tumor regions in liver whole-slide images. Sci. Rep., 11.","DOI":"10.1038\/s41598-020-80610-9"},{"key":"ref_35","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_36","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201312). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (2019). Searching for MobileNetV3. arXiv.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_38","unstructured":"Tan, M., and Le, Q.V. (2019, January 9\u201315). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, CA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/13\/3954\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:58:32Z","timestamp":1760032712000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/13\/3954"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,25]]},"references-count":38,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["s25133954"],"URL":"https:\/\/doi.org\/10.3390\/s25133954","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,25]]}}}