Bruce N Walker, Travis Wayne Blalock, Rebecca Leibowitz, Yoram Oron, Daphne Dascalu, Eli Omid David, Avi Dascalu
{"title":"通过结合音频和视觉卷积神经网络的机器学习，检测不同肤色的皮肤癌。","authors":"Bruce N Walker, Travis Wayne Blalock, Rebecca Leibowitz, Yoram Oron, Daphne Dascalu, Eli Omid David, Avi Dascalu","doi":"10.1159/000541573","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Skin cancer (SC) is common in fair skin (FS) at a 1:5 lifetime incidence for nonmelanoma skin cancer. In order to assist clinicians' decisions, a risk intervention technology was developed, which combines a dual-mode machine learning of visual and sonified (pixel to sound) data. The addition of an audio technology enhances malignant features of lesions, increases sensitivity and was previously validated under a prospective clinical setting in FS. In dark skin (DS), although rare by a 10-30 factor, skin cancer is diagnosed at more advanced stages resulting in a delayed diagnosis and affecting life quality and expectancy. It is known as well that SC diagnostic accuracy by machine learning in DS is decreased as compared to FS. The present study tests the use of sonification aided by artificial intelligence algorithms to compare diagnostics of different skin tones.</p><p><strong>Methodology: </strong>Biopsy-validated smartphone images were diagnosed in a retrospective study by a dual audio-visual convoluted neural network. A total of 60 Fitzpatrick I-III were compared to 72 Fitzpatrick IV-VI. A dichotomous diagnostic output, either malignant or benign, was assessed for sensitivity, specificity and area under the curves (AUCs) for the receiver operating characteristic (ROC) curve.</p><p><strong>Results: </strong>ROC curve analytics indicated an AUC of 0.858 (95% CI: 0.795-0.921) and 0.856 (95% CI: 0.759-0.953) for fair and DS (p = NS). Sensitivity of Fitzpatrick I-III skin and Fitzpatrick IV-VI were 84.4% (71.8-96.9) and 79.6% (63.4-93.8), respectively (p = NS). Specificity of Fitzpatrick I-III skin and Fitzpatrick IV-VI were 84.2% (72.6-95.8) and 85.3% (73.4-97.2), respectively (p = NS). The positive predictive and negative predictive values as well as accuracy (0.817 vs. 0.847) were all within the same range (p = NS).</p><p><strong>Conclusions: </strong>The results demonstrate that the dual-modality classifier identifies skin cancer of FS and DS similarly well. Sonification of malignant signs of a skin lesion demonstrates promising results, even with smartphone images, which should be considered as a tool to achieve more effective and accessible healthcare.</p>","PeriodicalId":19497,"journal":{"name":"Oncology","volume":" ","pages":"1-8"},"PeriodicalIF":2.5000,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Skin Cancer Detection in Diverse Skin Tones by Machine Learning Combining Audio and Visual Convolutional Neural Networks.\",\"authors\":\"Bruce N Walker, Travis Wayne Blalock, Rebecca Leibowitz, Yoram Oron, Daphne Dascalu, Eli Omid David, Avi Dascalu\",\"doi\":\"10.1159/000541573\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Skin cancer (SC) is common in fair skin (FS) at a 1:5 lifetime incidence for nonmelanoma skin cancer. In order to assist clinicians' decisions, a risk intervention technology was developed, which combines a dual-mode machine learning of visual and sonified (pixel to sound) data. The addition of an audio technology enhances malignant features of lesions, increases sensitivity and was previously validated under a prospective clinical setting in FS. In dark skin (DS), although rare by a 10-30 factor, skin cancer is diagnosed at more advanced stages resulting in a delayed diagnosis and affecting life quality and expectancy. It is known as well that SC diagnostic accuracy by machine learning in DS is decreased as compared to FS. The present study tests the use of sonification aided by artificial intelligence algorithms to compare diagnostics of different skin tones.</p><p><strong>Methodology: </strong>Biopsy-validated smartphone images were diagnosed in a retrospective study by a dual audio-visual convoluted neural network. A total of 60 Fitzpatrick I-III were compared to 72 Fitzpatrick IV-VI. A dichotomous diagnostic output, either malignant or benign, was assessed for sensitivity, specificity and area under the curves (AUCs) for the receiver operating characteristic (ROC) curve.</p><p><strong>Results: </strong>ROC curve analytics indicated an AUC of 0.858 (95% CI: 0.795-0.921) and 0.856 (95% CI: 0.759-0.953) for fair and DS (p = NS). Sensitivity of Fitzpatrick I-III skin and Fitzpatrick IV-VI were 84.4% (71.8-96.9) and 79.6% (63.4-93.8), respectively (p = NS). Specificity of Fitzpatrick I-III skin and Fitzpatrick IV-VI were 84.2% (72.6-95.8) and 85.3% (73.4-97.2), respectively (p = NS). The positive predictive and negative predictive values as well as accuracy (0.817 vs. 0.847) were all within the same range (p = NS).</p><p><strong>Conclusions: </strong>The results demonstrate that the dual-modality classifier identifies skin cancer of FS and DS similarly well. Sonification of malignant signs of a skin lesion demonstrates promising results, even with smartphone images, which should be considered as a tool to achieve more effective and accessible healthcare.</p>\",\"PeriodicalId\":19497,\"journal\":{\"name\":\"Oncology\",\"volume\":\" \",\"pages\":\"1-8\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1159/000541573\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1159/000541573","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
Skin Cancer Detection in Diverse Skin Tones by Machine Learning Combining Audio and Visual Convolutional Neural Networks.
Introduction: Skin cancer (SC) is common in fair skin (FS) at a 1:5 lifetime incidence for nonmelanoma skin cancer. In order to assist clinicians' decisions, a risk intervention technology was developed, which combines a dual-mode machine learning of visual and sonified (pixel to sound) data. The addition of an audio technology enhances malignant features of lesions, increases sensitivity and was previously validated under a prospective clinical setting in FS. In dark skin (DS), although rare by a 10-30 factor, skin cancer is diagnosed at more advanced stages resulting in a delayed diagnosis and affecting life quality and expectancy. It is known as well that SC diagnostic accuracy by machine learning in DS is decreased as compared to FS. The present study tests the use of sonification aided by artificial intelligence algorithms to compare diagnostics of different skin tones.
Methodology: Biopsy-validated smartphone images were diagnosed in a retrospective study by a dual audio-visual convoluted neural network. A total of 60 Fitzpatrick I-III were compared to 72 Fitzpatrick IV-VI. A dichotomous diagnostic output, either malignant or benign, was assessed for sensitivity, specificity and area under the curves (AUCs) for the receiver operating characteristic (ROC) curve.
Results: ROC curve analytics indicated an AUC of 0.858 (95% CI: 0.795-0.921) and 0.856 (95% CI: 0.759-0.953) for fair and DS (p = NS). Sensitivity of Fitzpatrick I-III skin and Fitzpatrick IV-VI were 84.4% (71.8-96.9) and 79.6% (63.4-93.8), respectively (p = NS). Specificity of Fitzpatrick I-III skin and Fitzpatrick IV-VI were 84.2% (72.6-95.8) and 85.3% (73.4-97.2), respectively (p = NS). The positive predictive and negative predictive values as well as accuracy (0.817 vs. 0.847) were all within the same range (p = NS).
Conclusions: The results demonstrate that the dual-modality classifier identifies skin cancer of FS and DS similarly well. Sonification of malignant signs of a skin lesion demonstrates promising results, even with smartphone images, which should be considered as a tool to achieve more effective and accessible healthcare.
期刊介绍：
Although laboratory and clinical cancer research need to be closely linked, observations at the basic level often remain removed from medical applications. This journal works to accelerate the translation of experimental results into the clinic, and back again into the laboratory for further investigation. The fundamental purpose of this effort is to advance clinically-relevant knowledge of cancer, and improve the outcome of prevention, diagnosis and treatment of malignant disease. The journal publishes significant clinical studies from cancer programs around the world, along with important translational laboratory findings, mini-reviews (invited and submitted) and in-depth discussions of evolving and controversial topics in the oncology arena. A unique feature of the journal is a new section which focuses on rapid peer-review and subsequent publication of short reports of phase 1 and phase 2 clinical cancer trials, with a goal of insuring that high-quality clinical cancer research quickly enters the public domain, regardless of the trial’s ultimate conclusions regarding efficacy or toxicity.
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