Deep Learning on 3D Cellular-resolution Tomogram
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 107
- 试验地点
- 1
- 主要终点
- Number of subjects of tomograms that can be analyzed by artificial intelligence techniques
研究概览
简要总结
Skin biopsy is the main method to diagnose skin tumors, skin inflammation, and pigmented diseases. However, biopsy is an invasive method that can cause wounds and scars. Optical coherent tomography (OCT) technology is a fast, non-invasive, non-radioactive, and label-free imaging method. This technology generates real-time images of living tissue by detecting the variations in the refractive indexes of various components in soft tissues. Recently, there is a breakthrough progress that the newly designed ultrahigh resolution OCT can provide in vivo cellular resolution similar to histopathological sections in the high magnification. In our previous clinical trial "Early feasibility study: application of OCT imaging in dermatology" (approved by IRB of MacKay Memorial Hospital, no. 17CT062Be), it showed characteristic features of different skin inflammatory diseases and tumors can be distinguished successfully in tomograms. There were no adverse event or serious adverse event in this trial. Artificial intelligence technologies have been used widely in the image analysis in recent years. Hence, we aim to collect OCT tomograms of common skin inflammatory diseases, skin tumors, and pigmented diseases, and compare with normal skin for machine learning. We expect the integration of tomograms with deep learning artificial intelligence may assist identifying histological features in these images and provide new alternative way for non-invasive diagnosis in dermatology.
详细描述
Introduction Optical coherent tomography (OCT) technology has been widely used in medical practice, such as ophthalmology. The application in dermatology is slowly progressed until the marked improvement of resolution recently. One of the newly designed OCT devices using in this study is based on the research and development of Professor Sheng-Lung Huang of National Taiwan University. The light source was made with original glass-covered crystalline fiber which has successfully provided sub-micron resolution on the skin, which is better than the traditional 5-10 micron resolution of high-definition OCT. This new OCT system (ApolloVue™ S100 image system, Viper1-S003, Apollo Medical Optics) has been used in this previous clinical trial "in vivo OCT images of different skin diseases" without adverse events. OCT images of different skin diseases collected in that trial were compared with HE-stained pathological sections. They provided useful information to physicians. The risk-benefit assessment of this clinical trial is the same as expected. The risk is low in clinical use, and both for the operators and the subjects. In recent years, the application of artificial intelligence technology in the analysis of tissue classification of medical images is rapidly developing. Therefore, we are going to use deep learning technology to improve the interpretation of OCT images to help the subsequent diagnosis of skin diseases.
Inclusion criteria
Experimental group:
- Adults aged 20 years or older
- Non-treat lesion of epidermal inflammatory disease: dermatitis and psoriasis: 300 participants.
- Benign tumors: seborrheic keratosis and nevus: 300 participants
- Malignant tumors: actinic keratosis (AK), melanoma, basal cell carcinoma (BCC), Bowen's disease, squamous cell carcinoma (SCC), and extramammary Paget's disease (EMPD): 100 participants
- Pigmented diseases: solar lentigo, melasma, and vitiligo: 300 participants
Control group:
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Prospective
入排标准
- 年龄范围
- 20 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- 未提供
排除标准
- 未提供
结局指标
主要结局
Number of subjects of tomograms that can be analyzed by artificial intelligence techniques
时间窗: 2.5 years
Number of subjects of tomograms that can be analyzed by artificial intelligence techniques (including machine learning and deep learning) will be compared to that cannot be analyzed to identify the feasibility of using artificial intelligence techniques to analyze tomograms at study completion.
Number of subjects with the similarity results of interpreting tomograms between artificial intelligence and experts
时间窗: 2.5 years
Number of subjects with the similarity results of interpreting tomograms between artificial intelligence and experts will be compared to that with no similarity to verify whether artificial intelligence interpretation are comparable with gold standard methods expert interpretation at study completion.
次要结局
- Number of subjects with the correlation between tomograms and gold standard methods, eg. existing clinical images or pathological images.(2.5 years)
研究者
Yu-Hung Wu
MD
Mackay Memorial Hospital