{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T06:16:49Z","timestamp":1787984209071,"version":"build-2784847793"},"reference-count":35,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T00:00:00Z","timestamp":1777161600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T00:00:00Z","timestamp":1777161600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Artificial Organs"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Individuals with visual prostheses often struggle to locate specific objects due to limited visual input. Current systems process visual information but fail to effectively highlight or prioritize objects based on user needs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study investigates a user\u2010centric object highlighting system designed to assist retinal prosthesis users in finding misplaced objects in a controlled experimental setting. The system uses pre\u2010trained multimodal models, allowing users to specify objects through spoken instructions, which are then highlighted in the camera frame. We evaluated the system's performance in a simulated environment with 18 sighted participants acting as virtual patients. We examined how various retinal implant resolutions and stimulation points, from low to high (60\u20131600 electrodes), impact object recognition.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Mixed\u2010effects logistic regression, both frequentist and Bayesian, revealed significant variance in recognition outcomes based on fixed and random effects. High\u2010resolution implants achieved the best and most consistent recognition rates, while lower\u2010resolution implants showed suboptimal recognition. Notably, across all implants, larger objects were recognized more effectively compared to smaller items, indicating that even higher\u2010resolution implants struggle with smaller objects. The characteristics of these objects, particularly size and distinct features, played a crucial role in their recognition performance. This underscores the necessity for effective detection systems tailored to the capabilities of implants, especially those with lower resolution, which lack sufficient detail for independent object recognition.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings provide valuable insights for enhancing user\u2010centric object highlighting systems and inform the development of real\u2010world testing in complex environments.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/aor.70147","type":"journal-article","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T05:45:50Z","timestamp":1777268750000},"page":"1264-1277","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating the Efficacy of Smart Saliency Detection System for Visual Prosthesis Users: An Experimental Comparison Across Various Visual Prosthesis Implants"],"prefix":"10.1111","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8030-6636","authenticated-orcid":false,"given":"Nermin","family":"Khalifa","sequence":"first","affiliation":[{"name":"School of Information Technology and Computer Science Nile University  Giza Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sahar","family":"Selim","sequence":"additional","affiliation":[{"name":"School of Information Technology and Computer Science Nile University  Giza Egypt"},{"name":"Medical Imaging and Image Processing Research Group, Center for Informatics Science Nile University  Giza Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Walid","family":"Al\u2010Atabany","sequence":"additional","affiliation":[{"name":"School of Information Technology and Computer Science Nile University  Giza Egypt"},{"name":"Medical Imaging and Image Processing Research Group, Center for Informatics Science Nile University  Giza Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,26]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2135485100"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.00036"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40942-023-00498-1"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/s23135782"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-36913-8"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.4274\/tjo.galenos.2022.53598"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11948-012-9375-6"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.visres.2009.02.003"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC46164.2021.9629663"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac6f82"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1167\/jov.22.2.20"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065722500526"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.85812"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-45416-4"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/BioCAS.2013.6679669"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/NILES59815.2023.10296708"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/3493826"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/BioCAS.2011.6107820"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aadd55"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2011.2177498"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1111\/aor.14824"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3458709.3458982"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1111\/aor.14022"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2013.63"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/10\/2\/026017"},{"key":"e_1_2_10_27_1","doi-asserted-by":"crossref","unstructured":"S.Liu Z.Zeng T.Ren et\u00a0al. \u201cGrounding Dino: Marrying Dino With Grounded Pre\u2010Training for Open\u2010Set Object Detection \u201darXiv Preprint arXiv:2303.05499 (2023).","DOI":"10.1007\/978-3-031-72970-6_3"},{"key":"e_1_2_10_28_1","doi-asserted-by":"crossref","unstructured":"L. G.Nadolskis L. M.Turkstra E.Larnyo andM.Beyeler \u201cGreat Expectations: Aligning Visual Prosthetic Development With Implantee Needs \u201dmedRxiv (2024).","DOI":"10.1101\/2024.03.12.24304186"},{"key":"e_1_2_10_29_1","first-page":"23318","volume-title":"International Conference on Machine Learning","author":"Wang P.","year":"2022"},{"key":"e_1_2_10_30_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1086"},{"key":"e_1_2_10_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_5"},{"key":"e_1_2_10_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-55729-3_11"},{"key":"e_1_2_10_33_1","doi-asserted-by":"crossref","unstructured":"T.\u2010Y.Lin M.Maire S.Belongie et\u00a0al. \u201cMicrosoft Coco: Common Objects in Context\u201d(2015).","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"e_1_2_10_34_1","doi-asserted-by":"crossref","unstructured":"M.Beyeler G. 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