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Hyperspectral imaging in automated digital dermoscopy screening for melanoma

机译:高光谱成像技术在自动化的数字dermoscopy筛查黑色素瘤

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Objectives Early melanoma detection decreases morbidity and mortality. Early detection classically involves dermoscopy to identify suspicious lesions for which biopsy is indicated. Biopsy and histological examination then diagnose benign nevi, atypical nevi, or cancerous growths. With current methods, a considerable number of unnecessary biopsies are performed as only 11% of all biopsied, suspicious lesions are actually melanomas. Thus, there is a need for more advanced noninvasive diagnostics to guide the decision of whether or not to biopsy. Artificial intelligence can generate screening algorithms that transform a set of imaging biomarkers into a risk score that can be used to classify a lesion as a melanoma or a nevus by comparing the score to a classification threshold. Melanoma imaging biomarkers have been shown to be spectrally dependent in Red, Green, Blue (RGB) color channels, and hyperspectral imaging may further enhance diagnostic power. The purpose of this study was to use the same melanoma imaging biomarkers previously described, but over a wider range of wavelengths to determine if, in combination with machine learning algorithms, this could result in enhanced melanoma detection. Methods We used the melanoma advanced imaging dermatoscope (mAID) to image pigmented lesions assessed by dermatologists as requiring a biopsy. The mAID is a 21‐wavelength imaging device in the 350–950?nm range. We then generated imaging biomarkers from these hyperspectral dermoscopy images, and, with the help of artificial intelligence algorithms, generated a melanoma Q‐score for each lesion (0?=?nevus, 1?=?melanoma). The Q‐score was then compared to the histopathologic diagnosis. Results The overall sensitivity and specificity of hyperspectral dermoscopy in detecting melanoma when evaluated in a set of lesions selected by dermatologists as requiring biopsy was 100% and 36%, respectively. Conclusion With widespread application, and if validated in larger clinical trials, this non‐invasive methodology could decrease unnecessary biopsies and potentially increase life‐saving early detection events. Lasers Surg. Med. 51:214–222, 2019. ? 2019 The Authors. Lasers in Surgery and Medicine Published by Wiley Periodicals, Inc.
机译:目标降低黑色素瘤早期检测发病率和死亡率。经典涉及dermoscopy识别可疑病变的活检。活组织检查和组织学检查诊断良性的痣,非典型痣或癌细胞生长。与现有方法,相当数量的不必要的活检是只有11%的所有检查,可疑病变黑色素瘤。先进的非侵入性诊断指导决定是否要活检。情报可以生成筛选算法将一组成像生物标记转换成风险评分,可用于分类病变作为黑素瘤或痣通过比较分数分类阈值。生物标记已经被证明是幽灵似地依赖的红,绿,蓝(RGB)的颜色渠道,高光谱图像可能会进一步提高诊断能力。黑色素瘤研究是使用相同的成像生物标志物前所述,但在一个更大的波长确定的范围结合机器学习算法,这可能导致增强黑色素瘤检测。我们使用了黑色素瘤先进的成像方法dermatoscope色素病变(服务员)图像由皮肤科医生评估需要活检。女仆是一个21波长成像设备中350 - 950吗?从这些高光谱dermoscopy生物标志物图片,在人工的帮助智能算法,生成的黑色素瘤问量得分为每个病变(0 = ?1 = ?黑素瘤)。病理学诊断。整体的敏感性和特异性高光谱dermoscopy检测黑色素瘤当评价一组病变选择皮肤科医生需要活检是100%和36%,分别。应用程序,如果在较大的临床验证试验,这种非侵入性方法可以减少不必要的活检和可能提高生活还是储蓄早期检测事件。激光Surg.地中海51:214 - 222,2019。作者。威利期刊,Inc。

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