{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T14:31:52Z","timestamp":1776954712826,"version":"3.51.4"},"reference-count":57,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T00:00:00Z","timestamp":1722816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Accurate evaluation of retinopathy of prematurity (ROP) severity is vital for screening and proper treatment. Current deep-learning-based automated AI systems for assessing ROP severity do not follow clinical guidelines and are opaque. The aim of this study is to develop an interpretable AI system by mimicking the clinical screening process to determine ROP severity level. A total of 6100 RetCam \u2162 wide-field digital retinal images were collected from Guangdong Women and Children Hospital at Panyu (PY) and Zhongshan Ophthalmic Center (ZOC). A total of 3330 images of 520 pediatric patients from PY were annotated to train an object detection model to detect lesion type and location. A total of 2770 images of 81 pediatric patients from ZOC were annotated for stage, zone, and the presence of plus disease. Integrating stage, zone, and the presence of plus disease according to clinical guidelines yields ROP severity such that an interpretable AI system was developed to provide the stage from the lesion type, the zone from the lesion location, and the presence of plus disease from a plus disease classification model. The ROP severity was calculated accordingly and compared with the assessment of a human expert. Our method achieved an area under the curve (AUC) of 0.95 (95% confidence interval [CI] 0.90\u20130.98) in assessing the severity level of ROP. Compared with clinical doctors, our method achieved the highest F1 score value of 0.76 in assessing the severity level of ROP. In conclusion, we developed an interpretable AI system for assessing the severity level of ROP that shows significant potential for use in clinical practice for ROP severity level screening.<\/jats:p>","DOI":"10.3390\/bioengineering11080792","type":"journal-article","created":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T15:45:22Z","timestamp":1722872722000},"page":"792","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["An Interpretable System for Screening the Severity Level of Retinopathy in Premature Infants Using Deep Learning"],"prefix":"10.3390","volume":"11","author":[{"given":"Wenhan","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2719-3733","authenticated-orcid":false,"given":"Hao","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Limei","family":"Sun","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5704-635X","authenticated-orcid":false,"given":"Songshan","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoling","family":"Luo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yili","family":"Jin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People\u2019s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjia","family":"Yan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Guangdong Women and Children Hospital, Guangzhou 511400, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7363-2274","authenticated-orcid":false,"given":"Jianxiang","family":"Deng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5589-4836","authenticated-orcid":false,"given":"Zhi","family":"Xie","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"He","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4936-3315","authenticated-orcid":false,"given":"Xiaoyan","family":"Ding","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e002267","DOI":"10.1136\/bmjpo-2023-002267","article-title":"Global, regional and national burden of retinopathy of prematurity among childhood and adolescent: A spatiotemporal analysis based on the Global Burden of Disease Study 2019","volume":"8","author":"Wang","year":"2024","journal-title":"BMJ Paediatr. Open"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1177\/1357633X20976028","article-title":"A cost-minimisation analysis comparing alternative telemedicine screening approaches for retinopathy of prematurity","volume":"29","author":"Kim","year":"2023","journal-title":"J. Telemed. Telecare"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1111\/ceo.13160","article-title":"Retinopathy of prematurity: The high cost of screening regional and remote infants","volume":"46","author":"Yu","year":"2018","journal-title":"Clin. Exp. Ophthalmol."},{"key":"ref_4","first-page":"110","article-title":"The Economic Model of Retinopathy of Prematurity (EcROP) Screening and Treatment: Mexico and the United States","volume":"168","author":"Rothschild","year":"2016","journal-title":"Arch. Ophthalmol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1016\/j.ajo.2012.04.003","article-title":"The societal burden of blindness secondary to retinopathy of prematurity in Lima, Peru","volume":"154","author":"Dave","year":"2012","journal-title":"Am. J. Ophthalmol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e186801","DOI":"10.1001\/jamanetworkopen.2018.6801","article-title":"Prevalence of severe visual disability among preterm children with retinopathy of prematurity and association with adherence to best practice guidelines","volume":"2","author":"Norman","year":"2019","journal-title":"JAMA Netw. Open"},{"key":"ref_7","first-page":"233","article-title":"Early Treatment for Retinopathy of Prematurity Cooperative Group. Final results of the Early Treatment for Retinopathy of Prematurity (ETROP) randomized trial","volume":"102","author":"Good","year":"2004","journal-title":"Trans. Am. Ophthalmol. Soc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e51","DOI":"10.1016\/j.ophtha.2021.05.031","article-title":"International Classification of Retinopathy of Prematurity, Third Edition","volume":"128","author":"Chiang","year":"2021","journal-title":"Ophthalmology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e2313674","DOI":"10.1001\/jamanetworkopen.2023.13674","article-title":"Integration of artificial intelligence decision aids to reduce workload and enhance efficiency in thyroid nodule management","volume":"6","author":"Tong","year":"2023","journal-title":"JAMA Netw. Open"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/s10916-023-02018-y","article-title":"Applications of Artificial Intelligence in Health Care Delivery","volume":"47","author":"Spear","year":"2023","journal-title":"J. Med. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1038\/s41597-024-03362-5","article-title":"A fundus image dataset for intelligent retinopathy of prematurity system","volume":"11","author":"Zhao","year":"2024","journal-title":"Sci. Data"},{"key":"ref_12","unstructured":"Yenice, E.K., Kara, C., and Erda\u015f, B. (2024). Automated detection of type 1 ROP, type 2 ROP and A-ROP based on deep learning. Eye, 1\u20135."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s13167-024-00350-y","article-title":"Automated detection of nine infantile fundus diseases and conditions in retinal images using a deep learning system","volume":"15","author":"Liu","year":"2024","journal-title":"EPMA J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1750","DOI":"10.1109\/TMI.2021.3065753","article-title":"Automatic staging for retinopathy of prematurity with deep feature fusion and ordinal classification strategy","volume":"40","author":"Peng","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1136\/bjophthalmol-2020-316526","article-title":"Automated detection of early-stage ROP using a deep convolutional neural network","volume":"105","author":"Huang","year":"2020","journal-title":"Br. J. Ophthalmol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1109\/TMI.2018.2863562","article-title":"Automated Analysis for Retinopathy of Prematurity by Deep Neural Networks","volume":"38","author":"Hu","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1186\/s40662-020-00206-2","article-title":"Automated identification of retinopathy of prematurity by image-based deep learning","volume":"7","author":"Tong","year":"2020","journal-title":"Eye Vis."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1016\/j.ophtha.2020.10.025","article-title":"Evaluation of a deep learning\u2013derived quantitative retinopathy of prematurity severity scale","volume":"128","author":"Campbell","year":"2021","journal-title":"Ophthalmology"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1001\/jamaophthalmol.2019.2433","article-title":"Monitoring disease progression with a quantitative severity scale for retinopathy of prematurity using deep learning","volume":"137","author":"Taylor","year":"2019","journal-title":"JAMA Ophthalmol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Huang, Y.-P., Vadloori, S., Chu, H.-C., Kang, E.Y.-C., Wu, W.-C., Kusaka, S., and Fukushima, Y. (2020). Deep learning models for automated diagnosis of retinopathy of prematurity in preterm infants. Electronics, 9.","DOI":"10.3390\/electronics9091444"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4087","DOI":"10.1364\/BOE.461411","article-title":"ADS-Net: Attention-awareness and deep supervision based network for automatic detection of retinopathy of prematurity","volume":"13","author":"Peng","year":"2022","journal-title":"Biomed. Opt. Express"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.ebiom.2018.08.033","article-title":"Automated retinopathy of prematurity screening using deep neural networks","volume":"35","author":"Wang","year":"2018","journal-title":"EBioMedicine"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e13267","DOI":"10.1049\/ell2.13267","article-title":"Applying novel self-supervised learning for early detection of retinopathy of prematurity","volume":"60","author":"Wang","year":"2024","journal-title":"Electron. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1136\/bjophthalmol-2018-313156","article-title":"Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity","volume":"103","author":"Redd","year":"2018","journal-title":"Br. J. Ophthalmol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e2217447","DOI":"10.1001\/jamanetworkopen.2022.17447","article-title":"Development and validation of a deep learning model to predict the occurrence and severity of retinopathy of prematurity","volume":"5","author":"Wu","year":"2022","journal-title":"JAMA Netw. Open"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"100417","DOI":"10.1016\/j.xops.2023.100417","article-title":"Oxygenation Fluctuations Associated with Severe Retinopathy of Prematurity: Insights from a Multimodal Deep Learning Approach","volume":"4","author":"Lin","year":"2024","journal-title":"Ophthalmol. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"109370","DOI":"10.1016\/j.compeleceng.2024.109370","article-title":"A review of Explainable Artificial Intelligence in healthcare","volume":"118","author":"Sadeghi","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"ref_28","first-page":"168","article-title":"Enhancing Transparency and Understanding in AI Decision-Making Processes","volume":"8","author":"Pillai","year":"2024","journal-title":"Iconic Res. Eng. J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s12559-023-10179-8","article-title":"Interpreting black-box models: A review on explainable artificial intelligence","volume":"16","author":"Hassija","year":"2023","journal-title":"Cogn. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1167\/tvst.11.5.17","article-title":"Early diagnosis and quantitative analysis of stages in retinopathy of prematurity based on deep convolutional neural networks","volume":"11","author":"Li","year":"2022","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"108516","DOI":"10.1016\/j.isci.2023.108516","article-title":"Development and validation of a semi-supervised deep learning model for automatic retinopathy of prematurity staging","volume":"27","author":"Feng","year":"2024","journal-title":"iScience"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ebrahimi, B., Le, D., Abtahi, M., Dadzie, A.K., Rossi, A., Rahimi, M., Son, T., Ostmo, S., Campbell, J.P., and Chan, R.V.P. (2024). Assessing spectral effectiveness in color fundus photography for deep learning classification of retinopathy of prematurity. J. Biomed. Opt., 29.","DOI":"10.1117\/1.JBO.29.7.076001"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e218758","DOI":"10.1001\/jamanetworkopen.2021.8758","article-title":"Automated explainable multidimensional deep learning platform of retinal images for retinopathy of prematurity screening","volume":"4","author":"Wang","year":"2021","journal-title":"JAMA Netw. Open"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, S., Zhang, R., Chen, G., Zhao, J., Wang, T., Zhang, G., and Lei, B. (2021, January 13\u201316). Attention-guided deep multi-instance learning for staging retinopathy of prematurity. Proceedings of the 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France.","DOI":"10.1109\/ISBI48211.2021.9434012"},{"key":"ref_35","unstructured":"Chen, S., Zhang, R., Chen, G., Zhao, J., Wang, T., Zhang, G., and Lei, B. (2019, January 17\u201320). Early detection of retinopathy of prematurity stage using deep learning approach. Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis 2019, San Diego, CA, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lei, H., Zhao, J., Xie, H., Liu, Y., Zhang, G., and Lei, B. (2023, January 5\u20138). Dual-branch Feature Interaction Network with Structure Information Learning for Retinopathy of Prematurity Classification. Proceedings of the 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Istanbul, Turkiye.","DOI":"10.1109\/BIBM58861.2023.10385575"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1364\/BOE.506119","article-title":"Automatic zoning for retinopathy of prematurity with a key area location system","volume":"15","author":"Peng","year":"2024","journal-title":"Biomed. Opt. Express"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1968","DOI":"10.1364\/BOE.447224","article-title":"Automatic zoning for retinopathy of prematurity with semi-supervised feature calibration adversarial learning","volume":"13","author":"Peng","year":"2022","journal-title":"Biomed. Opt. Express"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103530","DOI":"10.1109\/ACCESS.2019.2930120","article-title":"A deep learning framework for identifying zone I in RetCam images","volume":"7","author":"Zhao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_40","first-page":"1","article-title":"Deep learning-assisted retinopathy of prematurity (ROP) screening","volume":"4","author":"Kumar","year":"2023","journal-title":"ACM Trans. Comput. Health"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.bbe.2021.02.005","article-title":"A deep learning framework for the detection of Plus disease in retinal fundus images of preterm infants","volume":"41","author":"Ramachandran","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.compmedimag.2019.04.003","article-title":"A computer-aided diagnosis system for plus disease in retinopathy of prematurity with structure adaptive segmentation and vessel based features","volume":"74","author":"Nisha","year":"2019","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"VMR, S. (2024, January 17\u201319). Computer Aided Diagnostics in Prediction of Plus and Pre Plus Diseases of ROP in Neonatal Fundus Images. Proceedings of the 2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI), Chennai, India.","DOI":"10.1109\/RAEEUCCI61380.2024.10547740"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1167\/tvst.8.6.23","article-title":"Deep learning algorithm for automated diagnosis of retinopathy of prematurity plus disease","volume":"8","author":"Tan","year":"2019","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"E339","DOI":"10.1111\/aos.14264","article-title":"Automated diagnosis and quantitative analysis of plus disease in retinopathy of prematurity based on deep convolutional neural networks","volume":"98","author":"Mao","year":"2020","journal-title":"Acta Ophthalmol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1167\/tvst.9.2.10","article-title":"Plus disease in retinopathy of prematurity: Convolutional neural network performance using a combined neural network and feature extraction approach","volume":"9","author":"Yildiz","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"061107","DOI":"10.1117\/1.JMI.10.6.061107","article-title":"Image harmonization and deep learning automated classification of plus disease in retinopathy of prematurity","volume":"10","author":"Subramaniam","year":"2023","journal-title":"J. Med. Imaging"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1007\/s11042-023-15430-w","article-title":"Plus disease classification in Retinopathy of Prematurity using transform based features","volume":"83","author":"Jemshi","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"25259","DOI":"10.1007\/s10489-023-04766-3","article-title":"GFF-Net: Graph-based feature fusion network for diagnosing plus disease in retinopathy of prematurity","volume":"53","author":"Huang","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1287\/orsc.2021.1549","article-title":"To engage or not to engage with AI for critical judgments: How professionals deal with opacity when using AI for medical diagnosis","volume":"33","author":"Lebovitz","year":"2022","journal-title":"Organ. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s11017-021-09553-0","article-title":"What is morally at stake when using algorithms to make medical diagnoses? Expanding the discussion beyond risks and harms","volume":"42","author":"Kudina","year":"2021","journal-title":"Theor. Med. Bioeth."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1016\/j.cgh.2019.12.007","article-title":"Improving patient-provider relationships to improve health care","volume":"18","author":"Drossman","year":"2020","journal-title":"Clin. Gastroenterol. Hepatol."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zhou, H., Yang, W., Sun, L., Huang, L., Li, S., Luo, X., Jin, Y., Sun, W., Yan, W., and Li, J. (2024). RDLR: A Robust Deep Learning-Based Image Registration Method for Pediatric Retinal Images. J. Imaging Inform. Med., 1\u201315.","DOI":"10.1007\/s10278-024-01154-2"},{"key":"ref_54","unstructured":"Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., and Xu, J. (2019). MMDetection: Open mmlab detection toolbox and benchmark. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_57","unstructured":"Chu, C., Zhmoginov, A., and Sandler, M. (2017). Cyclegan, a master of steganography. arXiv."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/8\/792\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:30:17Z","timestamp":1760110217000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/8\/792"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,5]]},"references-count":57,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["bioengineering11080792"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering11080792","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,5]]}}}