NONINVASIVE DERMATOLOGICAL LESION CLASSIFIER

无创皮肤病病变分类器

• 批准号: 2645334
• 负责人: Eric R. Craine
• 金额: $ 10万
• 依托单位: WESTERN RESEARCH COMPANY, INC.
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-30 至 1999-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2645334
• 关键词: artificial intelligence; biomedical automation; biomedical equipment development; clinical research; diagnosis design /evaluation; fluorescence spectrometry; histology; human subject; neoplasm /cancer classification /staging; neoplasm /cancer diagnosis; noninvasive diagnosis; reflection spectrometry; skin neoplasms; spectrometry

Skin cancer is the fastest growing cancer in the United States today. Approximately 34,100 Americans developed cutaneous melanoma in 1995, and 7,200 died of the disease; of the survivors, many must contend with the ongoing trauma of disfigurement and fear. Skin biopsies are now the most frequently performed medical procedure reimbursed by Medicare. It is axiomatic among dermatologists that early detection and diagnosis are critical in the care and treatment of skin cancer patients. Great strides have been made in recent years in early detection of suspect skin lesions; however, the diagnosis remains based in the subjective evaluation of which skin lesions to biopsy. This decision is the basis of a great dilemma for physicians of at-risk patients who develop literally hundreds of lesions which could be pre-cancerous or cancerous. On one hand biopsies are expensive and traumatic; on the other, failure to biopsy the right lesion can lead to severe consequences. The dilemma is further exacerbated by the fact that 50-80% of biopsies prove unnecessary after the fact, contributing to an enormous of valuable health care dollars, patient trauma and negative patient behavior feedback. Recent developments in dermatological spectroscopy used to train an artificial neural net technology suggest that an automated clinical diagnostic aid which produces a quantitative rather than qualitative diagnostic assessment of skin lesions is possible. This project proposes development and testing of such a product. Spectroscopic samples of approximately 500 patients with abnormal skin lesions will be coupled with an equal number of normal skin spectra and used to train an artificial neural net classifier. This automated diagnostic aid will be tested against a large number of test samples for which a histological diagnosis is available for evaluation of the system. PROPOSED COMMERCIAL APPLICATIONS: The proposed project will lead to a non-invasive, in-office, real-time test to provide an automated, repeatable diagnostic probability of the nature of skin lesions prior to biopsy. Skin biopsies are now the most frequently performed reimbursed Medicare procedure, and as many as 50-80% are found not to be necessary after the fact. The low cost of this test, and rapid amortization of the system, coupled with the enormous health care cost savings possible in conjunction with a significant and widely recognized health problem, suggest that this product could have great commercial potential.

皮肤癌是当今美国增长最快的癌症。 1995 年，大约有 34,100 名美国人患有皮肤黑色素瘤， 7,200人死于该病；在幸存者中，许多人必须与 持续的毁容和恐惧创伤。 皮肤活检现在是最 经常进行的医疗程序由 Medicare 报销。 这是 皮肤科医生不言而喻，早期检测和诊断是 对于皮肤癌患者的护理和治疗至关重要。 伟大的 近年来在可疑皮肤的早期检测方面取得了长足进步 病变；然而，诊断仍然基于主观 评估哪些皮肤病变需要活检。 这个决定是依据 对于治疗高危患者的医生来说，这是一个巨大的困境 实际上有数百个可能是癌前或癌变的病变。 一方面，活组织检查费用昂贵且造成创伤；另一方面，另一方面，失败 对病变部位进行活检可能会导致严重的后果。 困境 50-80% 的活检证明这一事实进一步加剧了 事后不必要，贡献了巨大的价值 医疗费用、患者创伤和患者消极行为 反馈。 皮肤病学光谱学的最新发展用于 训练人工神经网络技术表明，自动化 临床诊断辅助工具产生定量而非 可以对皮肤病变进行定性诊断评估。 这 项目建议开发和测试此类产品。 约 500 名皮肤异常患者的光谱样本 病变将与同等数量的正常皮肤光谱相结合， 用于训练人工神经网络分类器。 这个自动化的 诊断辅助工具将针对大量测试样本进行测试 组织学诊断可用于系统评估。 拟议的商业应用： 拟议的项目将带来一种非侵入性、办公室内、实时的 测试提供自动化、可重复的诊断概率 活检前皮肤病变的性质。皮肤活检现在是最 经常进行医疗保险报销程序，高达 50-80% 事后发现没有必要。该测试成本低， 系统的快速摊销，加上巨大的健康状况 与显着且广泛的治疗相结合，可以节省护理成本 公认的健康问题，表明该产品可能有很大的作用 商业潜力。