International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun 130022, China.
Centre for Opto/Bio-Nano Measurement and Manufacturing, Zhongshan Institute of Changchun University of Science and Technology, Zhongshan 528400, China.
Anal Methods. 2024 Jul 11;16(27):4626-4635. doi: 10.1039/d4ay00684d.
Intelligent technology can assist in the diagnosis and treatment of disease, which would pave the way towards precision medicine in the coming decade. As a key focus of medical research, the diagnosis and prognosis of cancer play an important role in the future survival of patients. In this work, a diagnostic method based on nano-resolution imaging was proposed to meet the demand for precise detection methods in medicine and scientific research. The cell images scanned by AFM were recognized by cell feature engineering and machine learning classifiers. A feature ranking method based on the importance of features to responses was used to screen features closely related to categorization and optimization of feature combinations, which helps to understand the feature differences between cell types at the micro level. The results showed that the Bayesian optimized back propagation neural network has accuracy rates of 90.37% and 92.68% on two cell datasets (HL-7702 & SMMC-7721 and GES-1 & SGC-7901), respectively. This provides an automatic analysis method for identifying cancer cells or abnormal cells, which can help to reduce the burden of medical or scientific research, decrease misjudgment and promote precise medical care for the whole society.
智能技术可以辅助疾病的诊断和治疗，为未来十年的精准医学铺平道路。癌症的诊断和预后作为医学研究的重点，对患者的未来生存起着重要作用。在这项工作中，提出了一种基于纳米分辨率成像的诊断方法，以满足医学和科学研究中对精确检测方法的需求。通过原子力显微镜扫描的细胞图像，利用细胞特征工程和机器学习分类器进行识别。采用基于特征对响应重要性的特征排序方法，筛选与分类和特征组合优化密切相关的特征，有助于了解细胞类型在微观水平上的特征差异。结果表明，贝叶斯优化反向传播神经网络在两个细胞数据集（HL-7702 和 SMMC-7721 以及 GES-1 和 SGC-7901）上的准确率分别达到 90.37%和 92.68%。这为识别癌细胞或异常细胞提供了一种自动分析方法，可以帮助减轻医疗或科学研究的负担，减少误判，促进全社会的精准医疗。
