Enhancing Acute Promyelocytic Leukaemia Screening with an Artificial Intelligence-Based Digital Morphology Analyser: A Prospective Clinical Evaluation of MC-100i

Fan Zhang ¹ Pingjuan Liu ¹ Jieyu Zhan ² Jing Cheng ¹ Hongxia Tan ¹ Jiahang Zhang ³ Meiqi Song ³ Fengying Wu ⁴ Qiuyi Lin ⁴ Zhuangbiao Shi ⁴ Chanjun Yang ⁵ Meinan Wang ⁶ Qiu Li ⁷ Yang Wang ^1* Liubing Li ^1* JunXun Li ^1*

• ¹ Department of Medical Laboratory, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
• ² Department of Pediatrics, Guangzhou Baiyun District Maternal and Child Health Hospital, Guangzhou, Guangdong Province, China
• ³ School of Medical Technology, Guangdong Medical University, Dongguan, China
• ⁴ Yunkang school of medicine and health, Nanfang College, Guangzhou, China
• ⁵ Department of Blood Transfusion, the Second Affiliated Hospital of Shantou University Medical College, Shantou, China
• ⁶ Mindray Medical International Ltd, Shenzhen, China
• ⁷ School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China

Objectives: 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.Methods: 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.Conclusions: 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.