{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T13:34:27Z","timestamp":1785418467620,"version":"3.56.0"},"reference-count":39,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009619","name":"the Japan Agency for Medical Research and Development","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/004rtk039","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002241","name":"Japan Science and Technology Agency","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002241","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/00097mb19","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Japan Society for the Promotion of Science (JSPS) KAKENHI"},{"name":"New Energy and Industrial Technology Development Organization"},{"id":[{"id":"https:\/\/ror.org\/0055k7a87","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Background: Anemia is a common systemic condition associated with adverse maternal, perioperative, and cardiovascular outcomes. Although timely screening is clinically important, diagnosis still relies on invasive blood testing. Palpebral conjunctival pallor has traditionally been used as a noninvasive indicator of anemia, but its diagnostic accuracy remains limited. This study aimed to develop and validate a deep learning system to estimate hemoglobin (Hb) concentration and screen for anemia using palpebral conjunctiva images captured with a smartphone-compatible slit-lamp microscope. Methods: In this prospective observational study, 225 Japanese participants (20\u201392 years) underwent conjunctival imaging and blood testing. Palpebral conjunctiva videos were obtained using the Smart Eye Camera. Video frames were processed using automated anterior-segment segmentation and conjunctiva extraction. A ConvNeXt-based regression model was trained to predict Hb values. Anemia was defined using sex-specific Hb thresholds. Results: From 225 videos, 53,776 frames were extracted, yielding 9903 quality-filtered conjunctiva images (training: 8082; test: 1821). Video-level predicted Hb values moderately correlated with measured Hb (r = 0.42). For anemia screening, frame-level analysis achieved an AUC of 0.75, with accuracy of 0.76, sensitivity of 0.71, and specificity of 0.79. Video-level aggregation achieved 69% accuracy. Conclusions: Deep learning analysis of palpebral conjunctiva images acquired with a portable slit-lamp microscope demonstrated the feasibility of non-invasive hemoglobin estimation and anemia screening. Although the proposed approach achieved moderate performance, further improvements in model accuracy and prospective multi-center validation are required before clinical implementation.<\/jats:p>","DOI":"10.3390\/bioengineering13070824","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T12:00:17Z","timestamp":1784548817000},"page":"824","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of a Deep Learning Model to Estimate Anemia from Palpebral Conjunctiva Taken with a Portable Slit-Lamp Microscope"],"prefix":"10.3390","volume":"13","author":[{"given":"Yo","family":"Nakahara","sequence":"first","affiliation":[{"name":"Department of Extended Intelligence for Medicine, The Ishii-Ishibashi Laboratory, Keio University School of Medicine, Tokyo 1608582, Japan"},{"name":"Medimo Inc., Tokyo 1050001, Japan"},{"name":"OUI Inc., Tokyo 1070062, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3422-8578","authenticated-orcid":false,"given":"Eisuke","family":"Shimizu","sequence":"additional","affiliation":[{"name":"OUI Inc., Tokyo 1070062, Japan"},{"name":"Department of Ophthalmology, Keio University School of Medicine, Tokyo 1608582, Japan"},{"name":"Yokohama Keiai Eye Clinic, Yokohama 2400065, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7152-5176","authenticated-orcid":false,"given":"Takahiro","family":"Mizukami","sequence":"additional","affiliation":[{"name":"Yokohama Keiai Eye Clinic, Yokohama 2400065, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2832-4934","authenticated-orcid":false,"given":"Hiroki","family":"Nishimura","sequence":"additional","affiliation":[{"name":"OUI Inc., Tokyo 1070062, Japan"},{"name":"Department of Ophthalmology, Keio University School of Medicine, Tokyo 1608582, Japan"},{"name":"Yokohama Keiai Eye Clinic, Yokohama 2400065, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shintaro","family":"Nakayama","sequence":"additional","affiliation":[{"name":"OUI Inc., Tokyo 1070062, Japan"},{"name":"Department of Ophthalmology, Keio University School of Medicine, Tokyo 1608582, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5418-5387","authenticated-orcid":false,"given":"Tetsuo","family":"Ishikawa","sequence":"additional","affiliation":[{"name":"Department of Extended Intelligence for Medicine, The Ishii-Ishibashi Laboratory, Keio University School of Medicine, Tokyo 1608582, Japan"},{"name":"Division of Applied Mathematical Science, RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences, RIKEN, Yokohama 2300045, Japan"},{"name":"Collective Intelligence Research Laboratory, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo 1538902, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masatoshi","family":"Hirayama","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Keio University School of Medicine, Tokyo 1608582, Japan"},{"name":"Department of Ophthalmology, Faculty of Medicine, Fukuoka University, Fukuoka 8140180, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Risa","family":"Hokama","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Keio University School of Medicine, Tokyo 1608582, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazuhiro","family":"Sakurada","sequence":"additional","affiliation":[{"name":"Department of Extended Intelligence for Medicine, The Ishii-Ishibashi Laboratory, Keio University School of Medicine, Tokyo 1608582, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2892-9810","authenticated-orcid":false,"given":"Kazuno","family":"Negishi","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Keio University School of Medicine, Tokyo 1608582, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,17]]},"reference":[{"key":"ref_1","unstructured":"(2026, June 15). Haemoglobin Concentrations for the Diagnosis of Anaemia and Assessment of Severity. Vitamin and Mineral Nutrition Information System. Available online: https:\/\/iris.who.int\/server\/api\/core\/bitstreams\/b82789de-df63-41ba-8058-25f9f5da0c40\/content."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1017\/S1368980008002401","article-title":"Worldwide prevalence of anaemia, WHO Vitamin and Mineral Nutrition Information System, 1993\u20132005","volume":"12","author":"McLean","year":"2009","journal-title":"Public Health Nutr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1111\/nyas.14092","article-title":"Anemia epidemiology, pathophysiology, and etiology in low- and middle-income countries","volume":"1450","author":"Chaparro","year":"2019","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1182\/blood-2013-06-508325","article-title":"A systematic analysis of global anemia burden from 1990 to 2010","volume":"123","author":"Kassebaum","year":"2014","journal-title":"Blood"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.1111\/jog.14834","article-title":"Prenatal anemia and postpartum hemorrhage risk: A systematic review and meta-analysis","volume":"47","author":"Omotayo","year":"2021","journal-title":"J. Obstet. Gynaecol. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1002\/bjs.9861","article-title":"Meta-analysis of the association between preoperative anaemia and mortality after surgery","volume":"102","author":"Fowler","year":"2015","journal-title":"Br. J. Surg."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1016\/j.jacc.2008.04.061","article-title":"Anemia and mortality in heart failure patients: A systematic review and meta-analysis","volume":"52","author":"Groenveld","year":"2008","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1002\/jpn3.12454","article-title":"Approach to anaemia in gastrointestinal disease: A position paper by the ESPGHAN Gastroenterology Committee","volume":"80","author":"Broekaert","year":"2025","journal-title":"J. Pediatr. Gastroenterol. Nutr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1007\/s11606-006-5004-x","article-title":"The relation of conjunctival pallor to the presence of anemia","volume":"12","author":"Sheth","year":"1997","journal-title":"J. Gen. Intern. Med."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kalantri, A., Karambelkar, M., Joshi, R., and Kalantri, S. (2010). Accuracy and reliability of pallor for detecting anaemia: A hospital-based diagnostic accuracy study. PLoS ONE, 5.","DOI":"10.1371\/journal.pone.0008545"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.jemermed.2007.02.011","article-title":"Non-invasive determination of hemoglobin by digital photography of palpebral conjunctiva","volume":"33","author":"Suner","year":"2007","journal-title":"J. Emerg. Med."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Collings, S., Thompson, O., Hirst, E., Goossens, L., George, A., and Weinkove, R. (2016). Non-invasive detection of anaemia using digital photographs of the conjunctiva. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0153286"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"014019","DOI":"10.1117\/1.2167967","article-title":"Diffuse reflectance spectra of the palpebral conjunctiva and its utility as a noninvasive indicator of total hemoglobin","volume":"11","author":"McMurdy","year":"2006","journal-title":"J. Biomed. Opt."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e00192","DOI":"10.1002\/phy2.192","article-title":"Combined reflectance spectroscopy and stochastic modeling approach for noninvasive hemoglobin determination via palpebral conjunctiva","volume":"2","author":"Kim","year":"2014","journal-title":"Physiol. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1364\/OPTICA.390409","article-title":"mHealth spectroscopy of blood hemoglobin with spectral super-resolution","volume":"7","author":"Park","year":"2020","journal-title":"Optica"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4924","DOI":"10.1038\/s41467-018-07262-2","article-title":"Smartphone app for non-invasive detection of anemia using only patient-sourced photos","volume":"9","author":"Mannino","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_17","first-page":"535","article-title":"SmartHeLP: Smartphone-based Hemoglobin Level Prediction Using an Artificial Neural Network","volume":"2018","author":"Hasan","year":"2018","journal-title":"AMIA Annu. Symp. Proc."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Suner, S., Rayner, J., Ozturan, I.U., Hogan, G., Meehan, C.P., Chambers, A.B., Baird, J., and Jay, G.D. (2021). Prediction of anemia and estimation of hemoglobin concentration using a smartphone and machine learning techniques. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0253495"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhao, L., Vidwans, A., Bearnot, C.J., Rayner, J., Lin, T., Baird, J., Suner, S., and Jay, G.D. (2024). Prediction of anemia in real-time using a smartphone camera to record RAW images of the palpebral conjunctivae. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0302883"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shimizu, E., Ogawa, Y., Yazu, H., Aketa, N., Yang, F., Yamane, M., Sato, Y., Kawakami, Y., and Tsubota, K. (2019). \u201cSmart Eye Camera\u201d: An innovative technique to evaluate tear film breakup time in dry eye disease. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0215130"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shimizu, E., Yazu, H., Aketa, N., Yokoiwa, R., Sato, S., Yajima, J., Katayama, T., Sato, R., Tanji, M., and Sato, Y. (2021). A Study Validating the Estimation of Anterior Chamber Depth and Iridocorneal Angle with Portable and Non-Portable Slit-Lamp Microscopy. Sensors, 21.","DOI":"10.3390\/s21041436"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1167\/tvst.10.4.28","article-title":"Smart Eye Camera: A validation study for evaluating the tear film breakup time in human subjects","volume":"10","author":"Shimizu","year":"2021","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100906","DOI":"10.1016\/j.xops.2025.100906","article-title":"Validation of an Artificial Intelligence-Based Anterior Chamber Depth Estimation Using a Smartphone-Compatible Slit Lamp Device","volume":"6","author":"Mizukami","year":"2025","journal-title":"Ophthalmol. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.2147\/OPTH.S412233","article-title":"Interobserver Reliability of Tear Break-Up Time Examination Using \u201cSmart Eye Camera\u201d in Indonesian Remote Area","volume":"17","author":"Handayani","year":"2023","journal-title":"Clin. Ophthalmol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022, January 18\u201324). A ConvNet for the 2020s. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1111\/bjh.19621","article-title":"Machine\/deep learning-assisted hemoglobin level prediction using palpebral conjunctival images taken by a smartphone","volume":"205","author":"Kato","year":"2024","journal-title":"Br. J. Haematol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104038","DOI":"10.1016\/j.medengphy.2023.104038","article-title":"Non-invasive hemoglobin estimation from conjunctival images using deep learning","volume":"120","year":"2023","journal-title":"Med. Eng. Phys."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111026","DOI":"10.1016\/j.compbiomed.2025.111026","article-title":"Highly reliable personalized noninvasive hemoglobin estimation by using Vision Transformers and dual fine-tuning","volume":"197","author":"Camporeale","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"57","DOI":"10.4258\/hir.2025.31.1.57","article-title":"Deep learning model-based detection of anemia from conjunctiva images","volume":"31","author":"Sehar","year":"2025","journal-title":"Healthc. Inform. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"46968","DOI":"10.1109\/ACCESS.2018.2867110","article-title":"A new method and a non-invasive device to estimate anemia based on digital images of the conjunctiva","volume":"6","author":"Dimauro","year":"2018","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3738","DOI":"10.1038\/s41467-021-24116-6","article-title":"Preventing corneal blindness caused by keratitis using artificial intelligence","volume":"12","author":"Li","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1038\/s41551-018-0301-3","article-title":"Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy","volume":"2","author":"Wang","year":"2018","journal-title":"Nat. Biomed. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhao, X., Meng, L., Su, H., Lv, B., Lv, C., Xie, G., and Chen, Y. (2022). Deep-learning-based hemoglobin concentration prediction and anemia screening using ultra-wide field fundus images. Front. Cell Dev. Biol., 10.","DOI":"10.3389\/fcell.2022.888268"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1167\/tvst.14.1.20","article-title":"Noninvasive Anemia Detection and Hemoglobin Estimation from Retinal Images Using Deep Learning: A Scalable Solution for Resource-Limited Settings","volume":"14","author":"Khan","year":"2025","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_37","first-page":"103560","article-title":"Artificial intelligence for anemia screening, diagnosis, and management","volume":"74","author":"Olawade","year":"2025","journal-title":"Curr. Res. Transl. Med."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Navarro-Cabrera, J.R., Valles-Coral, M.A., Farro-Roque, M.E., Re\u00e1tegui-Lozano, N., and Ar\u00e9valo-Fasanando, L. (2025). Machine vision model using nail images for non-invasive detection of iron deficiency anemia in university students. Front. Big Data, 8.","DOI":"10.3389\/fdata.2025.1557600"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Mizukami, T., Sato, S., Asai, K., Inoue, T., Shimizu, E., Shimazaki, J., and Shimomura, Y. (2024). Evaluating the Effect of Image Enhancement on Diagnostic Reliability in Dry Eye Disease Using a Portable Imaging Device. Diagnostics, 14.","DOI":"10.20944\/preprints202410.0565.v1"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/7\/824\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T04:21:53Z","timestamp":1784607713000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/7\/824"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,17]]},"references-count":39,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["bioengineering13070824"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13070824","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,17]]}}}