Zhang Fan, Liu Pingjuan, Zhan Jieyu, Cheng Jing, Tan Hongxia, Zhang Jiahang, Song Meiqi, Wu Fengying, Lin Qiuyi, Shi Zhuangbiao, Yang Chanjun, Wang Meinan, Li Qiu, Wang Yang, Li Liubing, Li Junxun
Department of Medical Laboratory, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Department of Pediatric, Baiyun District Maternal and Child Healthcare Centre, Guangzhou, China.
Front Oncol. 2025 Apr 7;15:1572838. doi: 10.3389/fonc.2025.1572838. eCollection 2025.
Identification of abnormal promyelocytes is crucial for early diagnosis of Acute promyelocytic leukaemia (APL) and for reducing the early mortality rate of APL patients, which can be achieved by microscopic blood smear observation. However, microscopic observation has shortcomings, including interobserver variability and training difficulty. This is the first study evaluating the performance of MC-100i, an artificial intelligence (AI)-based digital morphology analyser, in identifying abnormal promyelocytes in blood smears and thus assisting in the early screening of APL.
One hundred ninety-two patients suspected of having APL were enrolled prospectively. The precision, accuracy, consistency with manual classification and turnaround time of MC-100i were studied in detail.
The precision of MC-100i in identifying all cell types was acceptable. MC-100i had excellent performance in preclassifying normal cell types, but its sensitivities for identifying blasts, abnormal promyelocytes, promyelocytes and neutrophilic myelocytes were relatively low, respectively. The Passing-Bablok and Bland-Altman tests revealed that the preclassification abnormal promyelocyte percentage obtained with MC-100i was proportionally different from that obtained with manual classification, whereas the postclassification and manual classification results were consistent. The clinical sensitivity and specificity for the early screening of APL were 95.8% and 100.0%, respectively. The turnaround and classification times were significantly shorter with the use of MC-100i for both the technologist and the experienced expert.
MC-100i is an effective tool for identifying abnormal promyelocytes in blood smears and assisting in the early screening of APL. It is useful when experienced morphological experts or advanced tests are not available.
识别异常早幼粒细胞对于急性早幼粒细胞白血病（APL）的早期诊断以及降低APL患者的早期死亡率至关重要，这可通过显微镜下血液涂片观察来实现。然而，显微镜观察存在缺点，包括观察者间差异和培训难度。这是第一项评估基于人工智能（AI）的数字形态分析仪MC - 100i在识别血液涂片中异常早幼粒细胞从而辅助APL早期筛查方面性能的研究。
前瞻性纳入192例疑似APL的患者。详细研究了MC - 100i的精密度、准确性、与手工分类的一致性以及周转时间。
MC - 100i识别所有细胞类型的精密度是可接受的。MC - 100i在预分类正常细胞类型方面表现出色，但其识别原始细胞、异常早幼粒细胞、早幼粒细胞和中性中幼粒细胞的敏感性相对较低。Passing - Bablok和Bland - Altman检验显示，MC - 100i获得的预分类异常早幼粒细胞百分比与手工分类获得的比例不同，而后分类和手工分类结果一致。APL早期筛查的临床敏感性和特异性分别为95.8%和100.0%。对于技术人员和经验丰富的专家而言，使用MC - 100i时周转和分类时间均显著缩短。
MC - 100i是识别血液涂片中异常早幼粒细胞并辅助APL早期筛查的有效工具。在没有经验丰富的形态学专家或先进检测手段时很有用。
