Deep Learning–Based Precision Cropping of Eye Regions in Strabismus Photographs: Algorithm Development and Validation Study for Workflow Optimization
Traditional ocular gaze photograph preprocessing, relying on manual cropping and head tilt correction, is time-consuming and inconsistent, limiting artificial intelligence (AI) model development and clinical application. This study aimed to address these challenges using an advanced preprocessing al...
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| Published in: | Journal of medical Internet research Vol. 27; no. 10; p. e74402 |
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| Main Authors: | , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Canada
Journal of Medical Internet Research
17.07.2025
JMIR Publications |
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| ISSN: | 1438-8871, 1439-4456, 1438-8871 |
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| Abstract | Traditional ocular gaze photograph preprocessing, relying on manual cropping and head tilt correction, is time-consuming and inconsistent, limiting artificial intelligence (AI) model development and clinical application.
This study aimed to address these challenges using an advanced preprocessing algorithm to enhance the accuracy, efficiency, and standardization of eye region cropping for clinical workflows and AI data preprocessing.
This retrospective and prospective cross-sectional study utilized 5832 images from 648 inpatients and outpatients, capturing 3 gaze positions under diverse conditions, including obstructions and varying distances. The preprocessing algorithm, based on a rotating bounding box detection framework, was trained and evaluated using precision, recall, and mean average precision (mAP) at various intersections over union thresholds. A 5-fold cross-validation was performed on an inpatient dataset, with additional testing on an independent outpatient dataset and an external cross-population dataset of 500 images from the IMDB-WIKI collection, representing diverse ethnicities and ages. Expert validation confirmed alignment with clinical standards across 96 images (48 images from a Chinese dataset of patients with strabismus and 48 images from IMDB-WIKI). Gradient-weighted class activation mapping heatmaps were used to assess model interpretability. A control experiment with 5 optometry specialists compared manual and automated cropping efficiency. Downstream task validation involved preprocessing 1000 primary gaze photographs using the Dlib toolkit, faster region-based convolutional neural network (R-CNN; both without head tilt correction), and our model (with correction), evaluating the impact of head tilt correction via the vision transformer strabismus screening network through 5-fold cross-validation.
The model achieved exceptional performance across datasets: on the 5-fold cross-validation set, it recorded a mean precision of 1.000 (95% CI 1.000-1.000), recall of 1.000 (95% CI 1.000-1.000), mAP50 of 0.995 (95% CI 0.995-0.995), and mAP95 of 0.893 (95% CI 0.870-0.918); on the internal independent test set, precision and recall were 1.000, with mAP50 of 0.995 and mAP95 of 0.801; and on the external cross-population test set, precision and recall were 1.000, with mAP50 of 0.937 and mAP95 of 0.792. The control experiment reduced image preparation time from 10 hours for manual cropping of 900 photos to 30 seconds with the automated model. Downstream strabismus screening task validation showed our model (with head tilt correction) achieving an area under the curve of 0.917 (95% CI 0.901-0.933), surpassing Dlib-toolkit and faster R-CNN (both without head tilt correction) with an area under the curve of 0.856 (P=.02) and 0.884 (P=.05), respectively. Heatmaps highlighted core ocular focus, aligning with head tilt directions.
This study delivers an AI-driven platform featuring a preprocessing algorithm that automates eye region cropping, correcting head tilt variations to improve image quality for AI development and clinical use. Integrated with electronic archives and patient-physician interaction, it enhances workflow efficiency, ensures telemedicine privacy, and supports ophthalmological research and strabismus care. |
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| AbstractList | Traditional ocular gaze photograph preprocessing, relying on manual cropping and head tilt correction, is time-consuming and inconsistent, limiting artificial intelligence (AI) model development and clinical application.BackgroundTraditional ocular gaze photograph preprocessing, relying on manual cropping and head tilt correction, is time-consuming and inconsistent, limiting artificial intelligence (AI) model development and clinical application.This study aimed to address these challenges using an advanced preprocessing algorithm to enhance the accuracy, efficiency, and standardization of eye region cropping for clinical workflows and AI data preprocessing.ObjectiveThis study aimed to address these challenges using an advanced preprocessing algorithm to enhance the accuracy, efficiency, and standardization of eye region cropping for clinical workflows and AI data preprocessing.This retrospective and prospective cross-sectional study utilized 5832 images from 648 inpatients and outpatients, capturing 3 gaze positions under diverse conditions, including obstructions and varying distances. The preprocessing algorithm, based on a rotating bounding box detection framework, was trained and evaluated using precision, recall, and mean average precision (mAP) at various intersections over union thresholds. A 5-fold cross-validation was performed on an inpatient dataset, with additional testing on an independent outpatient dataset and an external cross-population dataset of 500 images from the IMDB-WIKI collection, representing diverse ethnicities and ages. Expert validation confirmed alignment with clinical standards across 96 images (48 images from a Chinese dataset of patients with strabismus and 48 images from IMDB-WIKI). Gradient-weighted class activation mapping heatmaps were used to assess model interpretability. A control experiment with 5 optometry specialists compared manual and automated cropping efficiency. Downstream task validation involved preprocessing 1000 primary gaze photographs using the Dlib toolkit, faster region-based convolutional neural network (R-CNN; both without head tilt correction), and our model (with correction), evaluating the impact of head tilt correction via the vision transformer strabismus screening network through 5-fold cross-validation.MethodsThis retrospective and prospective cross-sectional study utilized 5832 images from 648 inpatients and outpatients, capturing 3 gaze positions under diverse conditions, including obstructions and varying distances. The preprocessing algorithm, based on a rotating bounding box detection framework, was trained and evaluated using precision, recall, and mean average precision (mAP) at various intersections over union thresholds. A 5-fold cross-validation was performed on an inpatient dataset, with additional testing on an independent outpatient dataset and an external cross-population dataset of 500 images from the IMDB-WIKI collection, representing diverse ethnicities and ages. Expert validation confirmed alignment with clinical standards across 96 images (48 images from a Chinese dataset of patients with strabismus and 48 images from IMDB-WIKI). Gradient-weighted class activation mapping heatmaps were used to assess model interpretability. A control experiment with 5 optometry specialists compared manual and automated cropping efficiency. Downstream task validation involved preprocessing 1000 primary gaze photographs using the Dlib toolkit, faster region-based convolutional neural network (R-CNN; both without head tilt correction), and our model (with correction), evaluating the impact of head tilt correction via the vision transformer strabismus screening network through 5-fold cross-validation.The model achieved exceptional performance across datasets: on the 5-fold cross-validation set, it recorded a mean precision of 1.000 (95% CI 1.000-1.000), recall of 1.000 (95% CI 1.000-1.000), mAP50 of 0.995 (95% CI 0.995-0.995), and mAP95 of 0.893 (95% CI 0.870-0.918); on the internal independent test set, precision and recall were 1.000, with mAP50 of 0.995 and mAP95 of 0.801; and on the external cross-population test set, precision and recall were 1.000, with mAP50 of 0.937 and mAP95 of 0.792. The control experiment reduced image preparation time from 10 hours for manual cropping of 900 photos to 30 seconds with the automated model. Downstream strabismus screening task validation showed our model (with head tilt correction) achieving an area under the curve of 0.917 (95% CI 0.901-0.933), surpassing Dlib-toolkit and faster R-CNN (both without head tilt correction) with an area under the curve of 0.856 (P=.02) and 0.884 (P=.05), respectively. Heatmaps highlighted core ocular focus, aligning with head tilt directions.ResultsThe model achieved exceptional performance across datasets: on the 5-fold cross-validation set, it recorded a mean precision of 1.000 (95% CI 1.000-1.000), recall of 1.000 (95% CI 1.000-1.000), mAP50 of 0.995 (95% CI 0.995-0.995), and mAP95 of 0.893 (95% CI 0.870-0.918); on the internal independent test set, precision and recall were 1.000, with mAP50 of 0.995 and mAP95 of 0.801; and on the external cross-population test set, precision and recall were 1.000, with mAP50 of 0.937 and mAP95 of 0.792. The control experiment reduced image preparation time from 10 hours for manual cropping of 900 photos to 30 seconds with the automated model. Downstream strabismus screening task validation showed our model (with head tilt correction) achieving an area under the curve of 0.917 (95% CI 0.901-0.933), surpassing Dlib-toolkit and faster R-CNN (both without head tilt correction) with an area under the curve of 0.856 (P=.02) and 0.884 (P=.05), respectively. Heatmaps highlighted core ocular focus, aligning with head tilt directions.This study delivers an AI-driven platform featuring a preprocessing algorithm that automates eye region cropping, correcting head tilt variations to improve image quality for AI development and clinical use. Integrated with electronic archives and patient-physician interaction, it enhances workflow efficiency, ensures telemedicine privacy, and supports ophthalmological research and strabismus care.ConclusionsThis study delivers an AI-driven platform featuring a preprocessing algorithm that automates eye region cropping, correcting head tilt variations to improve image quality for AI development and clinical use. Integrated with electronic archives and patient-physician interaction, it enhances workflow efficiency, ensures telemedicine privacy, and supports ophthalmological research and strabismus care. Background Traditional ocular gaze photograph preprocessing, relying on manual cropping and head tilt correction, is time-consuming and inconsistent, limiting artificial intelligence (AI) model development and clinical application. Objective This study aimed to address these challenges using an advanced preprocessing algorithm to enhance the accuracy, efficiency, and standardization of eye region cropping for clinical workflows and AI data preprocessing. Methods This retrospective and prospective cross-sectional study utilized 5832 images from 648 inpatients and outpatients, capturing 3 gaze positions under diverse conditions, including obstructions and varying distances. The preprocessing algorithm, based on a rotating bounding box detection framework, was trained and evaluated using precision, recall, and mean average precision (mAP) at various intersections over union thresholds. A 5-fold cross-validation was performed on an inpatient dataset, with additional testing on an independent outpatient dataset and an external cross-population dataset of 500 images from the IMDB-WIKI collection, representing diverse ethnicities and ages. Expert validation confirmed alignment with clinical standards across 96 images (48 images from a Chinese dataset of patients with strabismus and 48 images from IMDB-WIKI). Gradient-weighted class activation mapping heatmaps were used to assess model interpretability. A control experiment with 5 optometry specialists compared manual and automated cropping efficiency. Downstream task validation involved preprocessing 1000 primary gaze photographs using the Dlib toolkit, faster region-based convolutional neural network (R-CNN; both without head tilt correction), and our model (with correction), evaluating the impact of head tilt correction via the vision transformer strabismus screening network through 5-fold cross-validation. Results The model achieved exceptional performance across datasets: on the 5-fold cross-validation set, it recorded a mean precision of 1.000 (95% CI 1.000‐1.000), recall of 1.000 (95% CI 1.000‐1.000), mAP50 of 0.995 (95% CI 0.995‐0.995), and mAP95 of 0.893 (95% CI 0.870‐0.918); on the internal independent test set, precision and recall were 1.000, with mAP50 of 0.995 and mAP95 of 0.801; and on the external cross-population test set, precision and recall were 1.000, with mAP50 of 0.937 and mAP95 of 0.792. The control experiment reduced image preparation time from 10 hours for manual cropping of 900 photos to 30 seconds with the automated model. Downstream strabismus screening task validation showed our model (with head tilt correction) achieving an area under the curve of 0.917 (95% CI 0.901‐0.933), surpassing Dlib-toolkit and faster R-CNN (both without head tilt correction) with an area under the curve of 0.856 (P =.02) and 0.884 (P =.05), respectively. Heatmaps highlighted core ocular focus, aligning with head tilt directions. Conclusions This study delivers an AI-driven platform featuring a preprocessing algorithm that automates eye region cropping, correcting head tilt variations to improve image quality for AI development and clinical use. Integrated with electronic archives and patient-physician interaction, it enhances workflow efficiency, ensures telemedicine privacy, and supports ophthalmological research and strabismus care. Traditional ocular gaze photograph preprocessing, relying on manual cropping and head tilt correction, is time-consuming and inconsistent, limiting artificial intelligence (AI) model development and clinical application. This study aimed to address these challenges using an advanced preprocessing algorithm to enhance the accuracy, efficiency, and standardization of eye region cropping for clinical workflows and AI data preprocessing. This retrospective and prospective cross-sectional study utilized 5832 images from 648 inpatients and outpatients, capturing 3 gaze positions under diverse conditions, including obstructions and varying distances. The preprocessing algorithm, based on a rotating bounding box detection framework, was trained and evaluated using precision, recall, and mean average precision (mAP) at various intersections over union thresholds. A 5-fold cross-validation was performed on an inpatient dataset, with additional testing on an independent outpatient dataset and an external cross-population dataset of 500 images from the IMDB-WIKI collection, representing diverse ethnicities and ages. Expert validation confirmed alignment with clinical standards across 96 images (48 images from a Chinese dataset of patients with strabismus and 48 images from IMDB-WIKI). Gradient-weighted class activation mapping heatmaps were used to assess model interpretability. A control experiment with 5 optometry specialists compared manual and automated cropping efficiency. Downstream task validation involved preprocessing 1000 primary gaze photographs using the Dlib toolkit, faster region-based convolutional neural network (R-CNN; both without head tilt correction), and our model (with correction), evaluating the impact of head tilt correction via the vision transformer strabismus screening network through 5-fold cross-validation. The model achieved exceptional performance across datasets: on the 5-fold cross-validation set, it recorded a mean precision of 1.000 (95% CI 1.000‐1.000), recall of 1.000 (95% CI 1.000‐1.000), mAP50 of 0.995 (95% CI 0.995‐0.995), and mAP95 of 0.893 (95% CI 0.870‐0.918); on the internal independent test set, precision and recall were 1.000, with mAP50 of 0.995 and mAP95 of 0.801; and on the external cross-population test set, precision and recall were 1.000, with mAP50 of 0.937 and mAP95 of 0.792. The control experiment reduced image preparation time from 10 hours for manual cropping of 900 photos to 30 seconds with the automated model. Downstream strabismus screening task validation showed our model (with head tilt correction) achieving an area under the curve of 0.917 (95% CI 0.901‐0.933), surpassing Dlib-toolkit and faster R-CNN (both without head tilt correction) with an area under the curve of 0.856 (P =.02) and 0.884 (P =.05), respectively. Heatmaps highlighted core ocular focus, aligning with head tilt directions. This study delivers an AI-driven platform featuring a preprocessing algorithm that automates eye region cropping, correcting head tilt variations to improve image quality for AI development and clinical use. Integrated with electronic archives and patient-physician interaction, it enhances workflow efficiency, ensures telemedicine privacy, and supports ophthalmological research and strabismus care. Traditional ocular gaze photograph preprocessing, relying on manual cropping and head tilt correction, is time-consuming and inconsistent, limiting artificial intelligence (AI) model development and clinical application. This study aimed to address these challenges using an advanced preprocessing algorithm to enhance the accuracy, efficiency, and standardization of eye region cropping for clinical workflows and AI data preprocessing. This retrospective and prospective cross-sectional study utilized 5832 images from 648 inpatients and outpatients, capturing 3 gaze positions under diverse conditions, including obstructions and varying distances. The preprocessing algorithm, based on a rotating bounding box detection framework, was trained and evaluated using precision, recall, and mean average precision (mAP) at various intersections over union thresholds. A 5-fold cross-validation was performed on an inpatient dataset, with additional testing on an independent outpatient dataset and an external cross-population dataset of 500 images from the IMDB-WIKI collection, representing diverse ethnicities and ages. Expert validation confirmed alignment with clinical standards across 96 images (48 images from a Chinese dataset of patients with strabismus and 48 images from IMDB-WIKI). Gradient-weighted class activation mapping heatmaps were used to assess model interpretability. A control experiment with 5 optometry specialists compared manual and automated cropping efficiency. Downstream task validation involved preprocessing 1000 primary gaze photographs using the Dlib toolkit, faster region-based convolutional neural network (R-CNN; both without head tilt correction), and our model (with correction), evaluating the impact of head tilt correction via the vision transformer strabismus screening network through 5-fold cross-validation. The model achieved exceptional performance across datasets: on the 5-fold cross-validation set, it recorded a mean precision of 1.000 (95% CI 1.000-1.000), recall of 1.000 (95% CI 1.000-1.000), mAP50 of 0.995 (95% CI 0.995-0.995), and mAP95 of 0.893 (95% CI 0.870-0.918); on the internal independent test set, precision and recall were 1.000, with mAP50 of 0.995 and mAP95 of 0.801; and on the external cross-population test set, precision and recall were 1.000, with mAP50 of 0.937 and mAP95 of 0.792. The control experiment reduced image preparation time from 10 hours for manual cropping of 900 photos to 30 seconds with the automated model. Downstream strabismus screening task validation showed our model (with head tilt correction) achieving an area under the curve of 0.917 (95% CI 0.901-0.933), surpassing Dlib-toolkit and faster R-CNN (both without head tilt correction) with an area under the curve of 0.856 (P=.02) and 0.884 (P=.05), respectively. Heatmaps highlighted core ocular focus, aligning with head tilt directions. This study delivers an AI-driven platform featuring a preprocessing algorithm that automates eye region cropping, correcting head tilt variations to improve image quality for AI development and clinical use. Integrated with electronic archives and patient-physician interaction, it enhances workflow efficiency, ensures telemedicine privacy, and supports ophthalmological research and strabismus care. Abstract BackgroundTraditional ocular gaze photograph preprocessing, relying on manual cropping and head tilt correction, is time-consuming and inconsistent, limiting artificial intelligence (AI) model development and clinical application. ObjectiveThis study aimed to address these challenges using an advanced preprocessing algorithm to enhance the accuracy, efficiency, and standardization of eye region cropping for clinical workflows and AI data preprocessing. MethodsThis retrospective and prospective cross-sectional study utilized 5832 images from 648 inpatients and outpatients, capturing 3 gaze positions under diverse conditions, including obstructions and varying distances. The preprocessing algorithm, based on a rotating bounding box detection framework, was trained and evaluated using precision, recall, and mean average precision (mAP) at various intersections over union thresholds. A 5-fold cross-validation was performed on an inpatient dataset, with additional testing on an independent outpatient dataset and an external cross-population dataset of 500 images from the IMDB-WIKI collection, representing diverse ethnicities and ages. Expert validation confirmed alignment with clinical standards across 96 images (48 images from a Chinese dataset of patients with strabismus and 48 images from IMDB-WIKI). Gradient-weighted class activation mapping heatmaps were used to assess model interpretability. A control experiment with 5 optometry specialists compared manual and automated cropping efficiency. Downstream task validation involved preprocessing 1000 primary gaze photographs using the Dlib toolkit, faster region-based convolutional neural network (R-CNN; both without head tilt correction), and our model (with correction), evaluating the impact of head tilt correction via the vision transformer strabismus screening network through 5-fold cross-validation. ResultsThe model achieved exceptional performance across datasets: on the 5-fold cross-validation set, it recorded a mean precision of 1.000 (95% CI 1.000‐1.000), recall of 1.000 (95% CI 1.000‐1.000), mAP50 of 0.995 (95% CI 0.995‐0.995), and mAP95 of 0.893 (95% CI 0.870‐0.918); on the internal independent test set, precision and recall were 1.000, with mAP50 of 0.995 and mAP95 of 0.801; and on the external cross-population test set, precision and recall were 1.000, with mAP50 of 0.937 and mAP95 of 0.792. The control experiment reduced image preparation time from 10 hours for manual cropping of 900 photos to 30 seconds with the automated model. Downstream strabismus screening task validation showed our model (with head tilt correction) achieving an area under the curve of 0.917 (95% CI 0.901‐0.933), surpassing Dlib-toolkit and faster R-CNN (both without head tilt correction) with an area under the curve of 0.856 (PP ConclusionsThis study delivers an AI-driven platform featuring a preprocessing algorithm that automates eye region cropping, correcting head tilt variations to improve image quality for AI development and clinical use. Integrated with electronic archives and patient-physician interaction, it enhances workflow efficiency, ensures telemedicine privacy, and supports ophthalmological research and strabismus care. |
| Audience | Academic |
| Author | Chen, Xiaohang Wu, Dawen Xie, Bin Liu, Longqian Li, Yanfei Liu, Jingyu Yang, Guoyuan Shang, Wenyi Yang, Zeyi Zhang, Haixian Yin, Teng |
| Author_xml | – sequence: 1 givenname: Dawen orcidid: 0000-0002-4938-3044 surname: Wu fullname: Wu, Dawen – sequence: 2 givenname: Yanfei orcidid: 0009-0003-4563-7515 surname: Li fullname: Li, Yanfei – sequence: 3 givenname: Zeyi orcidid: 0009-0007-2327-9082 surname: Yang fullname: Yang, Zeyi – sequence: 4 givenname: Teng orcidid: 0009-0003-1254-6705 surname: Yin fullname: Yin, Teng – sequence: 5 givenname: Xiaohang orcidid: 0000-0002-0165-3607 surname: Chen fullname: Chen, Xiaohang – sequence: 6 givenname: Jingyu orcidid: 0009-0000-7153-397X surname: Liu fullname: Liu, Jingyu – sequence: 7 givenname: Wenyi orcidid: 0009-0001-5148-9358 surname: Shang fullname: Shang, Wenyi – sequence: 8 givenname: Bin orcidid: 0009-0004-1144-9900 surname: Xie fullname: Xie, Bin – sequence: 9 givenname: Guoyuan orcidid: 0000-0002-7314-836X surname: Yang fullname: Yang, Guoyuan – sequence: 10 givenname: Haixian orcidid: 0000-0002-9821-508X surname: Zhang fullname: Zhang, Haixian – sequence: 11 givenname: Longqian orcidid: 0000-0002-2265-4625 surname: Liu fullname: Liu, Longqian |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40674714$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1038/s41433-024-03228-5 10.1109/CVPR.2016.91 10.1007/978-3-319-46448-0_2 10.1109/ICCV.2017.322 10.1371/journal.pone.0255643 10.3760/cma.j.cn112142-20231111-00226 10.1016/s0161-6420(99)00035-4 10.2196/60226 10.3389/ebm.2024.10320 10.3390/make5040083 10.1109/ICCV.2015.169 10.1109/EHB47216.2019.8970033 10.1109/CVPR.2014.241 10.1186/s12911-021-01691-8 10.3390/diagnostics9030072 10.1109/ICCVW.2015.41 10.1136/bjo.2007.116905 10.1167/tvst.10.1.33 10.1007/s11263-019-01228-7 |
| ContentType | Journal Article |
| Copyright | Dawen Wu, Yanfei Li, Zeyi Yang, Teng Yin, Xiaohang Chen, Jingyu Liu, Wenyi Shang, Bin Xie, Guoyuan Yang, Haixian Zhang, Longqian Liu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org). COPYRIGHT 2025 Journal of Medical Internet Research Copyright © Dawen Wu, Yanfei Li, Zeyi Yang, Teng Yin, Xiaohang Chen, Jingyu Liu, Wenyi Shang, Bin Xie, Guoyuan Yang, Haixian Zhang, Longqian Liu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org) 2025 |
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| Keywords | AI management system image preprocessing ocular alignment artificial intelligence clinical workflow |
| Language | English |
| License | Dawen Wu, Yanfei Li, Zeyi Yang, Teng Yin, Xiaohang Chen, Jingyu Liu, Wenyi Shang, Bin Xie, Guoyuan Yang, Haixian Zhang, Longqian Liu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org). This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
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| PublicationDate | 2025-07-17 |
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| PublicationTitle | Journal of medical Internet research |
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| References | Tan (R24); 21 Huang (R15); 16 Ünver (R23); 9 Wu (R6); 249 Zheng (R18); 10 King (R8) Terven (R13); 5 Chia (R1); 91 R21 R20 R22 R25 Wu (R5); 38 Liu (R3); 60 Ming (R4); 26 R7 R9 Selvaraju (R14); 128 R10 Coats (R2); 107 R12 R11 R16 R17 R19 |
| References_xml | – volume: 38 start-page: 3101 issue: 16 ident: R5 article-title: An artificial intelligence platform for the screening and managing of strabismus publication-title: Eye (Lond) doi: 10.1038/s41433-024-03228-5 – ident: R19 doi: 10.1109/CVPR.2016.91 – ident: R20 doi: 10.1007/978-3-319-46448-0_2 – ident: R21 – ident: R17 doi: 10.1109/ICCV.2017.322 – volume: 16 issue: 8 ident: R15 article-title: An automatic screening method for strabismus detection based on image processing publication-title: PLoS One doi: 10.1371/journal.pone.0255643 – volume: 60 start-page: 484 issue: 6 ident: R3 article-title: Challenges and prospects in the application of artificial intelligence for ocular disease screening and diagnosis publication-title: Zhonghua Yan Ke Za Zhi doi: 10.3760/cma.j.cn112142-20231111-00226 – ident: R25 – volume: 107 start-page: 402 issue: 2 ident: R2 article-title: Impact of large angle horizontal strabismus on ability to obtain employment publication-title: Ophthalmology doi: 10.1016/s0161-6420(99)00035-4 – ident: R8 publication-title: J Mach Learn Res – volume: 26 ident: R4 article-title: Performance of ChatGPT in ophthalmic registration and clinical diagnosis: cross-sectional study publication-title: J Med Internet Res doi: 10.2196/60226 – volume: 249 ident: R6 article-title: Integrating artificial intelligence in strabismus management: current research landscape and future directions publication-title: Exp Biol Med (Maywood) doi: 10.3389/ebm.2024.10320 – ident: R10 – ident: R16 – volume: 5 start-page: 1680 issue: 4 ident: R13 article-title: A comprehensive review of YOLO architectures in computer vision: from YOLOv1 to YOLOv8 and YOLO-NAS publication-title: MAKE doi: 10.3390/make5040083 – ident: R9 doi: 10.1109/ICCV.2015.169 – ident: R12 – ident: R22 doi: 10.1109/EHB47216.2019.8970033 – ident: R7 doi: 10.1109/CVPR.2014.241 – volume: 21 issue: 1 ident: R24 article-title: Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification publication-title: BMC Med Inform Decis Mak doi: 10.1186/s12911-021-01691-8 – volume: 9 issue: 3 ident: R23 article-title: Skin lesion segmentation in dermoscopic images with combination of YOLO and GrabCut algorithm publication-title: Diagnostics (Basel) doi: 10.3390/diagnostics9030072 – ident: R11 doi: 10.1109/ICCVW.2015.41 – volume: 91 start-page: 1337 issue: 10 ident: R1 article-title: Comitant horizontal strabismus: an Asian perspective publication-title: Br J Ophthalmol doi: 10.1136/bjo.2007.116905 – volume: 10 start-page: 33 issue: 1 ident: R18 article-title: Detection of referable horizontal strabismus in children’s primary gaze photographs using deep learning publication-title: Transl Vis Sci Technol doi: 10.1167/tvst.10.1.33 – volume: 128 start-page: 336 issue: 2 ident: R14 article-title: Grad-CAM: visual explanations from deep networks via gradient-based localization publication-title: Int J Comput Vis doi: 10.1007/s11263-019-01228-7 |
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| Snippet | Traditional ocular gaze photograph preprocessing, relying on manual cropping and head tilt correction, is time-consuming and inconsistent, limiting artificial... Background Traditional ocular gaze photograph preprocessing, relying on manual cropping and head tilt correction, is time-consuming and inconsistent, limiting... Abstract BackgroundTraditional ocular gaze photograph preprocessing, relying on manual cropping and head tilt correction, is time-consuming and inconsistent,... |
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| SubjectTerms | Adult Algorithms Artificial Intelligence Assistive Technology for Vision Loss/Impairment Care and treatment Cross-Sectional Studies Deep Learning Diagnosis Eye - diagnostic imaging Female Health aspects Humans Image Processing, Computer-Assisted - methods Machine Learning Male Medical screening Methods Middle Aged Ophthalmology Original Paper Photography Prospective Studies Retrospective Studies Strabismus Strabismus - diagnostic imaging Tools, Programs and Algorithms Workflow |
| Title | Deep Learning–Based Precision Cropping of Eye Regions in Strabismus Photographs: Algorithm Development and Validation Study for Workflow Optimization |
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