[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127791-en":3,"doc-seo-127791-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127791,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","The Development and Performance of a Machine-Learning Based Mobile Platform for Visually Determining Penile Pathology Etiology - Research report","Machine-learning algorithms can enable low-cost, user-guided mobile visual diagnostic platforms to address disparities in access to sexual health services. A clinical image dataset was built using original and augmented images for five penile diseases: herpes eruption, syphilitic chancres, penile candidiasis, penile cancer, and genital warts. The approach combined U-Net semantic pixel segmentation with an Inception-ResNet v2 classifier and a GradCAM++ salience map, trained on 91% of images and evaluated on 9% using recall, precision, specificity, and F1-score.","The Development and Performance of a Machine-Learning Based Mobile Platform for Visually Determining the Etiology of  \nPenile Pathology  \nLao-Tzu Allan-Blitz MD MPH1*, Sithira Ambepitiya MBBS2, Raghavendra Tirupathi MD3, Jeffrey D. Klausner MD MPH4, Yudara Kularathne MD, FAMS2  \n(1) . Division of Global Health Equity: Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA  \n(2) . HeHealth Inc. San Francisco, CA, USA  \n(3) . Keystone Infectious Diseases, Keystone Health, Chambersburg, PA, USA  \n(4) . Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA  \nDisclosures: LAB and JDK received consulting fees from HeHealth Inc. SA is a Medical Executive at HeHealth Inc.  \nAcknowledgements and Funding: A startup, HeHealth has raised funding from institutional investors and angel investors and would like to specifically acknowledge Plug and Play Tech Center as well as ARKRAY Corporate Venture Capital.  \nCorresponding Author Contact Information:  \nLao-Tzu Allan-Blitz  \nDepartment of Medicine, Brigham and Women’s Hospital 75 Francis Street Boston, MA 02115, USA  \nEmail: [lallan-blitz@partners.org](lallan-blitz@partners.org), phone: (805) 896-5313  \nAlternative Author Information:  \nProf. Jeffrey D. Klausner  \nDepartment of Population and Public Health Sciences Keck School of Medicine University of Southern California Los Angeles, CA 90033, USA  \nEmail: [jdklausner@med.usc.edu](jdklausner@med.usc.edu)  \nAbstract  \nMachine-learning algorithms can facilitate low-cost, user-guided visual diagnostic platforms for addressing disparities in access to sexual health services. We developed a clinical image dataset using original and augmented images for five penile diseases: herpes eruption, syphilitic chancres, penile candidiasis, penile cancer, and genital warts. We used a U-net architecture model for semantic pixel segmentation into background or subject image, the Inception-ResNet version 2 neural architecture to classify each pixel as diseased or non-diseased, and a salience map using GradCAM++ . We trained the model on a random 91% sample of the image database using 150 epochs per image, and evaluated the model on the remaining 9% of images, assessing recall (or sensitivity), precision, specificity, and F1-score (accuracy) . Of the 239 images in the validation dataset, 45 (18.8%) were of genital warts, 43 (18.0%) were of HSV infection, 29 (12.1%) were of penile cancer, 40 (16.7%) were of penile candidiasis, 37 (15.5%) were of syphilitic chancres, and 45 (18.8%) were of non-diseased penises. The overall accuracy of the model for correctly classifying the diseased image was 0.944. Between July 1stand October 1st 2023, there were 2,640 unique users of the mobile platform. Among a random sample of submissions (n=437), 271 (62.0%) were from the United States, 64 (14.6%) from Singapore, 41 (9.4%) from Candia, 40 (9.2%) from the United Kingdom, and 21 (4.8%) from Vietnam. The majority (n=277 [63.4%]) were between 18 and 30 years old. We report on the development of a machine-learning model for classifying five penile diseases, which demonstrated excellent performance on a validation dataset. That model is currently in use globally and has the potential to improve access to diagnostic services for penile diseases.  \nKeywords: Machine-Learning; Sexually Transmitted Infections; Visual Diagnostics; Mobile Technology; Health Equity  \nResearch In Context  \nEvidence before this study  \nAccess to diagnostic services is a major barrier to sexual health care, and has numerous drivers, which include insufficient laboratory infrastructure in low-resource settings, lack of locally available health centers in rural areas, and stigma around sexually transmitted infections, which limits careseeking. Machine-learning algorithms are increasingly being utilized in healthcare contexts, and can be used to visually categorize disease states.  \nAdded value of this study  \nWe d","cbCaimcKkPbq6BAd","https://ap.wps.com/l/cbCaimcKkPbq6BAd","pdf",615861,1,12,"English","en",105,"# Abstract\n# Research In Context\n## Evidence before this study\n## Added value of this study\n## Implications of all the available evidence\n# Introduction","[{\"question\":\"What diseases does the machine-learning platform classify?\",\"answer\":\"The platform is designed to classify five penile diseases: herpes eruption, syphilitic chancres, penile candidiasis, penile cancer, and genital warts.\"},{\"question\":\"How was the model trained and evaluated?\",\"answer\":\"The model was trained on a random 91% sample of the image database and evaluated on the remaining 9%, using metrics including recall (sensitivity), precision, specificity, and F1-score.\"},{\"question\":\"What performance did the model achieve on the validation dataset?\",\"answer\":\"On the validation set, the overall accuracy for correctly classifying diseased images was 0.944, with class-specific counts reported for each pathology category.\"}]","The Development and Performance of a Machine-Learning Based Mobile Platform for Visually Determining Penile Pathology Etiology - 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