人工智能技术在组织和细胞形态学评估中的应用

形态学评估和特殊染色评分系统是目前基础和临床科研的重要组成部分，对于判断药物疗效和基因干预效果非常重要。然而，目前的视觉评分系统主观性强、重复性差、准确率低，极易出现漏诊和误诊。基于深度学习的人工智能技术有望克服这一问题，利用卷积神经网络能准确提取与肿瘤患者疗效和预后相关的内部特征，如肿瘤-间质比、神经侵犯和淋巴细胞空间分布；图像化和数字化显示药物干预疾病进展的疗效，同时能对与临床治疗、分型和预后相关的分子标志物进行定量化和自动化评分。人工智能技术应用于组织和细胞形态学评估后，将会推动临床药物评价和基础科研评价的一致性、重复性和准确性，有望进一步推动医学科研的发展。
Presently morphological evaluation and special staining scoring system are important components of basic and clinical research, and are very important for judging the efficacy of drugs and gene intervention. However, the current visual scoring system has some disadvantages such as strong subjectivity, poor repeatability and low accuracy, and is prone to missed diagnosis and misdiagnosis. Artificial intelligence technology based on deep learning is expected to overcome these problems. In our study, we found that the convolutional neural network can be used to accurately extract internal features related to the treatment and prognosis of tumors, such as tumor-stroma ratio, nerve invasion and spatial distribution of lymphatic cells in tumor specimens, visualizing and digitalizing the curative effect of drug intervention on disease progression, and can quantify and automatic evaluate the expression of molecular biomarkers related to clinical treatment, classification and prognosis. The application of artificial intelligence technology in tissue and cell morphology assessment will promote the consistency, repeatability and accuracy of clinical drug evaluation and basic scientific research evaluation, and is expected to further promote the development of medical research