{"id":680931,"date":"2020-07-31T01:42:22","date_gmt":"2020-07-31T08:42:22","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=680931"},"modified":"2020-07-31T01:42:22","modified_gmt":"2020-07-31T08:42:22","slug":"a-deep-learning-system-for-automated-angle-closure-detection-in-anterior-segment-optical-coherence-tomography-images","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-deep-learning-system-for-automated-angle-closure-detection-in-anterior-segment-optical-coherence-tomography-images\/","title":{"rendered":"A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images"},"content":{"rendered":"
\n
\n

Purpose<\/h3>\n

Anterior segment optical coherence tomography (AS-OCT) provides an objective imaging modality for visually identifying anterior segment structures. An automated detection system could assist ophthalmologists in interpreting AS-OCT images for the presence of angle closure.<\/p>\n<\/div>\n

\n

Design<\/h3>\n

Development of an artificial intelligence automated detection system for the presence of angle closure.<\/p>\n<\/div>\n

Methods<\/h3>\n

A deep learning system for automated angle-closure detection in AS-OCT images was developed, and this was compared with another automated angle-closure detection system based on quantitative features. A total of 4135 Visante AS-OCT images from 2113 subjects (8270 anterior chamber angle images with 7375 open-angle and 895 angle-closure) were examined. The deep learning angle-closure detection system for a 2-class classification problem was tested by 5-fold cross-validation. The deep learning system and the automated angle-closure detection system based on quantitative features were evaluated against clinicians’ grading of AS-OCT images as the reference standard.<\/p>\n<\/div>\n

\n

Results<\/h3>\n

The area under the receiver operating characteristic curve of the system using quantitative features was 0.90 (95% confidence interval [CI] 0.891\u20130.914) with a sensitivity of 0.79 \u00b1 0.037 and a specificity of 0.87 \u00b1 0.009, while the area under the receiver operating characteristic curve of the deep learning system was 0.96 (95% CI 0.953\u20130.968) with a sensitivity of 0.90 \u00b1 0.02 and a specificity of 0.92 \u00b1 0.008, against clinicians’ grading of AS-OCT images as the reference standard.<\/p>\n<\/div>\n

\n

Conclusions<\/h3>\n

The results demonstrate the potential of the deep learning system for angle-closure detection in AS-OCT images.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Purpose Anterior segment optical coherence tomography (AS-OCT) provides an objective imaging modality for visually identifying anterior segment structures. An automated detection system could assist ophthalmologists in interpreting AS-OCT images for the presence of angle closure. Design Development of an artificial intelligence automated detection system for the presence of angle closure. Methods A deep learning system […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"American Journal of Ophthalmology","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"37","msr_page_range_end":"45","msr_series":"","msr_volume":"203","msr_copyright":"","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2019-7","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":0,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13562,13553],"msr-publication-type":[193715],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-680931","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-computer-vision","msr-research-area-medical-health-genomics","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2019-7","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"American Journal of Ophthalmology","msr_volume":"203","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S000293941930087X","label_id":"243109","label":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Huazhu Fu","user_id":0,"rest_url":false},{"type":"text","value":"Mani Baskaran","user_id":0,"rest_url":false},{"type":"text","value":"Yanwu Xu","user_id":0,"rest_url":false},{"type":"edited_text","value":"Stephen Lin","user_id":33735,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Stephen Lin"},{"type":"text","value":"Damon Wing Kee Wong","user_id":0,"rest_url":false},{"type":"text","value":"Jiang Liu","user_id":0,"rest_url":false},{"type":"text","value":"Tin A. 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