{"id":884439,"date":"2022-10-10T14:47:59","date_gmt":"2022-10-10T21:47:59","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2022-10-18T15:07:09","modified_gmt":"2022-10-18T22:07:09","slug":"dermoscopy-diagnosis-of-cancerous-lesions-utilizing-dual-deep-learning-algorithms-via-visual-and-audio-sonification-outputs-laboratory-and-prospective-observational-studies","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/dermoscopy-diagnosis-of-cancerous-lesions-utilizing-dual-deep-learning-algorithms-via-visual-and-audio-sonification-outputs-laboratory-and-prospective-observational-studies\/","title":{"rendered":"Dermoscopy diagnosis of cancerous lesions utilizing dual deep learning algorithms via visual and audio (sonification) outputs: Laboratory and prospective observational studies"},"content":{"rendered":"

Abstract<\/h2>\n
\n

Background<\/h3>\n

Early diagnosis of skin cancer lesions by\u00a0dermoscopy<\/a>, the gold standard in dermatological imaging, calls for a diagnostic upscale. The aim of the study was to improve the accuracy of dermoscopic skin cancer diagnosis through use of novel deep learning (DL) algorithms. An additional sonification-derived diagnostic layer was added to the visual classification to increase sensitivity.<\/p>\n<\/div>\n

\n

Methods<\/h3>\n

Two parallel studies were conducted: a laboratory retrospective study (LABS,\u00a0n<\/em>\u202f=\u202f482 biopsies) and a non-interventional prospective observational study (OBS,\u00a0n<\/em>\u202f=\u202f63 biopsies). A training data set of biopsy-verified reports, normal and cancerous skin lesions (n<\/em>\u202f=\u202f3954), were used to develop a DL classifier exploring visual features (System A). The outputs of the classifier were sonified, i.e. data conversion into sound (System B). Derived sound files were analyzed by a second machine learning classifier, either as raw audio (LABS, OBS) or following conversion into spectrograms (LABS) and by image analysis and human heuristics (OBS). The OBS criteria outcomes were System A specificity and System B sensitivity as raw sounds, spectrogram areas or heuristics.<\/p>\n<\/div>\n

\n

Findings<\/h3>\n

LABS employed dermoscopies, half benign half malignant, and compared the accuracy of Systems A and B. System A algorithm resulted in a ROC AUC of 0.976 (95% CI, 0.965\u20130.987). Secondary machine learning analysis of raw sound, FFT and Spectrogram ROC curves resulted in AUC’s of 0.931 (95% CI 0.881\u20130.981), 0.90 (95% CI 0.838\u20130.963) and 0.988 (CI 95% 0.973\u20131.001), respectively. OBS analysis of raw sound dermoscopies by the secondary machine learning resulted in a ROC AUC of 0.819 (95% CI, 0.7956 to 0.8406). OBS image analysis of AUC for spectrograms displayed a ROC AUC of 0.808 (CI 95% 0.6945 To 0.9208). By applying a heuristic analysis of Systems A and B a sensitivity of 86% and specificity of 91% were derived in the clinical study.<\/p>\n<\/div>\n

\n

Interpretation<\/h3>\n

Adding a\u00a0second stage<\/a>\u00a0of processing, which includes a deep learning algorithm of sonification and heuristic inspection with machine learning, significantly improves diagnostic accuracy. A combined two-stage system is expected to assist clinical decisions and de-escalate the current trend of over-diagnosis of skin cancer lesions as pathological.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Abstract Background Early diagnosis of skin cancer lesions by\u00a0dermoscopy, the gold standard in dermatological imaging, calls for a diagnostic upscale. The aim of the study was to improve the accuracy of dermoscopic skin cancer diagnosis through use of novel deep learning (DL) algorithms. An additional sonification-derived diagnostic layer was added to the visual classification to […]<\/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":"eBioMedicine","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"176","msr_page_range_end":"183","msr_series":"","msr_volume":"40","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":"2019-2-1","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":false,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13556,243062,13554,13553],"msr-publication-type":[193715],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[246673,248587,266238],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-884439","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-audio-acoustics","msr-research-area-human-computer-interaction","msr-research-area-medical-health-genomics","msr-locale-en_us","msr-field-of-study-artificial-neural-network","msr-field-of-study-perception","msr-field-of-study-sonification"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2019-2-1","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"eBioMedicine","msr_volume":"40","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":0,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2352396419300337","label_id":"243109","label":0},{"type":"doi","viewUrl":"false","id":"false","title":"10.1016\/j.ebiom.2019.01.028","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":"Bruce N. 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