Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Institute of Image Processing & Pattern Recognition, Department of Automation, Shanghai Jiao Tong University, Shanghai, China; Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.
Lab Invest. 2023 Apr;103(4):100055. doi: 10.1016/j.labinv.2022.100055. Epub 2023 Jan 10.
A morphologic examination is essential for the diagnosis of hematological diseases. However, its conventional manual operation is time-consuming and laborious. Herein, we attempt to establish an artificial intelligence (AI)-aided diagnostic framework integrating medical expertise. This framework acts as a virtual hematological morphologist (VHM) for diagnosing hematological neoplasms. Two datasets were established as follows: An image dataset was used to train the Faster Region-based Convolutional Neural Network to develop an image-based morphologic feature extraction model. A case dataset containing retrospective morphologic diagnostic data was used to train a support vector machine algorithm to develop a feature-based case identification model based on diagnostic criteria. Integrating these 2 models established a whole-process AI-aided diagnostic framework, namely, VHM, and a 2-stage strategy was applied to practice case diagnosis. The recall and precision of VHM in bone marrow cell classification were 94.65% and 93.95%, respectively. The balanced accuracy, sensitivity, and specificity of VHM were 97.16%, 99.09%, and 92%, respectively, in the differential diagnosis of normal and abnormal cases, and 99.23%, 97.96%, and 100%, respectively, in the precise diagnosis of chronic myelogenous leukemia in chronic phase. This work represents the first attempt, to our knowledge, to extract multimodal morphologic features and to integrate a feature-based case diagnosis model for designing a comprehensive AI-aided morphologic diagnostic framework. The performance of our knowledge-based framework was superior to that of the widely used end-to-end AI-based diagnostic framework in terms of testing accuracy (96.88% vs 68.75%) or generalization ability (97.11% vs 68.75%) in differentiating normal and abnormal cases. The remarkable advantage of VHM is that it follows the logic of clinical diagnostic procedures, making it a reliable and interpretable hematological diagnostic tool.
形态学检查对于血液病的诊断至关重要。然而，其传统的手动操作既费时又费力。在此，我们尝试建立一个集成医学专业知识的人工智能（AI）辅助诊断框架。该框架充当虚拟血液形态学家（VHM），用于诊断血液系统肿瘤。我们建立了两个数据集：一个图像数据集用于训练基于区域的快速卷积神经网络，以开发基于图像的形态学特征提取模型；一个包含回顾性形态学诊断数据的病例数据集，用于训练支持向量机算法，以根据诊断标准开发基于特征的病例识别模型。整合这 2 个模型建立了一个全过程 AI 辅助诊断框架，即 VHM，并应用 2 阶段策略进行实际病例诊断。VHM 在骨髓细胞分类中的召回率和精确率分别为 94.65%和 93.95%。VHM 在正常和异常病例鉴别诊断中的平衡准确率、敏感度和特异性分别为 97.16%、99.09%和 92%，在慢性期慢性髓性白血病的精确诊断中分别为 99.23%、97.96%和 100%。这是首次尝试提取多模态形态学特征，并整合基于特征的病例诊断模型，设计全面的 AI 辅助形态学诊断框架。就测试准确性（96.88%对 68.75%）或泛化能力（97.11%对 68.75%）而言，我们基于知识的框架的性能优于广泛使用的端到端 AI 诊断框架，用于区分正常和异常病例。VHM 的显著优势在于它遵循临床诊断程序的逻辑，使其成为一种可靠且可解释的血液学诊断工具。
