Machine Learning-based Complementary Artificial Intelligence Model for Dermoscopic Diagnosis of Pigmented Skin Lesions in Resource-limited Settings.

IF 1.8 Q3 SURGERY
Plastic and Reconstructive Surgery Global Open Pub Date : 2025-07-28 eCollection Date: 2025-07-01 DOI:10.1097/GOX.0000000000007004
Ryohei Kaneko, Satoshi Akaishi, Rei Ogawa, Hiroaki Kuwahara
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引用次数: 0

Abstract

Background: Rapid advancements in big data and machine learning have expanded their application in healthcare, introducing sophisticated diagnostics to settings with limited medical resources. Notably, free artificial intelligence (AI) services that require no programming skills are now accessible to healthcare professionals, allowing those in underresourced areas to leverage AI technology. This study aimed to evaluate the potential of these accessible services for diagnosing pigmented skin tumors, underscoring the democratization of advanced medical technologies.

Methods: In this experimental diagnostic study, we collected 400 dermoscopic images (100 per tumor type) labeled through supervised learning from pathologically confirmed cases. The images were split into training, validation, and testing datasets (8:1:1 ratio) and uploaded to Vertex AI for model training. Supervised learning was performed using the Google Cloud Platform, Vertex AI, based on pathological diagnoses. The model's performance was assessed using confusion matrices and precision-recall curves.

Results: The AI model achieved an average recall rate of 86.3%, precision rate of 87.3%, accuracy of 86.3%, and F1 score of 0.87. Misclassification rates were less than 20% for each category. Accuracy was 80% for malignant melanoma and 100% for both basal cell carcinoma and seborrheic keratosis. Testing on separate cases yielded an accuracy of approximately 70%.

Conclusions: The metrics obtained in this study suggest that the model can reliably assist in the diagnostic process, even for practitioners without prior AI expertise. The study demonstrated that free AI tools can accurately classify pigmented skin lesions with minimal expertise, potentially providing high-precision diagnostic support in settings lacking dermatologists.

基于机器学习的互补人工智能模型在资源有限的环境下用于色素皮肤病变的皮肤镜诊断。
背景:大数据和机器学习的快速发展扩大了它们在医疗保健领域的应用,为医疗资源有限的环境引入了复杂的诊断方法。值得注意的是,医疗保健专业人员现在可以获得不需要编程技能的免费人工智能(AI)服务,使资源不足地区的人们能够利用人工智能技术。本研究旨在评估这些可获得的服务在诊断色素皮肤肿瘤方面的潜力,强调先进医疗技术的民主化。方法:在本实验诊断研究中,我们从病理确诊病例中收集400张皮肤镜图像(每种肿瘤类型100张),通过监督学习标记。图像被分成训练、验证和测试数据集(8:1:1的比例),并上传到Vertex AI进行模型训练。基于病理诊断,使用谷歌云平台,Vertex AI进行监督学习。使用混淆矩阵和查准率-查全率曲线来评估模型的性能。结果:人工智能模型平均召回率为86.3%,准确率为87.3%,准确率为86.3%,F1得分为0.87。每个类别的误分类率低于20%。恶性黑色素瘤的准确率为80%,基底细胞癌和脂溢性角化病的准确率为100%。对不同情况的测试产生了大约70%的准确率。结论:本研究中获得的指标表明,该模型可以可靠地协助诊断过程,即使对于没有事先人工智能专业知识的从业者也是如此。该研究表明,免费的人工智能工具可以用最少的专业知识准确地对色素沉着的皮肤病变进行分类,可能在缺乏皮肤科医生的环境中提供高精度的诊断支持。
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来源期刊
CiteScore
2.20
自引率
13.30%
发文量
1584
审稿时长
10 weeks
期刊介绍: Plastic and Reconstructive Surgery—Global Open is an open access, peer reviewed, international journal focusing on global plastic and reconstructive surgery.Plastic and Reconstructive Surgery—Global Open publishes on all areas of plastic and reconstructive surgery, including basic science/experimental studies pertinent to the field and also clinical articles on such topics as: breast reconstruction, head and neck surgery, pediatric and craniofacial surgery, hand and microsurgery, wound healing, and cosmetic and aesthetic surgery. Clinical studies, experimental articles, ideas and innovations, and techniques and case reports are all welcome article types. Manuscript submission is open to all surgeons, researchers, and other health care providers world-wide who wish to communicate their research results on topics related to plastic and reconstructive surgery. Furthermore, Plastic and Reconstructive Surgery—Global Open, a complimentary journal to Plastic and Reconstructive Surgery, provides an open access venue for the publication of those research studies sponsored by private and public funding agencies that require open access publication of study results. Its mission is to disseminate high quality, peer reviewed research in plastic and reconstructive surgery to the widest possible global audience, through an open access platform. As an open access journal, Plastic and Reconstructive Surgery—Global Open offers its content for free to any viewer. Authors of articles retain their copyright to the materials published. Additionally, Plastic and Reconstructive Surgery—Global Open provides rapid review and publication of accepted papers.
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