Artificial intelligence-assisted early screening of acute promyelocytic leukaemia in blood smears: a prospective evaluation of MC-100i
ObjectivesIdentification 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, includi...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1572838/full |
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| author | Fan Zhang Pingjuan Liu Jieyu Zhan Jing Cheng Hongxia Tan Jiahang Zhang Meiqi Song Fengying Wu Qiuyi Lin Zhuangbiao Shi Chanjun Yang Meinan Wang Qiu Li Yang Wang Liubing Li Junxun Li |
| author_facet | Fan Zhang Pingjuan Liu Jieyu Zhan Jing Cheng Hongxia Tan Jiahang Zhang Meiqi Song Fengying Wu Qiuyi Lin Zhuangbiao Shi Chanjun Yang Meinan Wang Qiu Li Yang Wang Liubing Li Junxun Li |
| author_sort | Fan Zhang |
| collection | DOAJ |
| description | ObjectivesIdentification 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.MethodsOne 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.ResultsThe 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.ConclusionsMC-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. |
| format | Article |
| id | doaj-art-0c9c2db55e5648bcad3c91b39f572abd |
| institution | DOAJ |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-0c9c2db55e5648bcad3c91b39f572abd2025-08-20T03:17:55ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-04-011510.3389/fonc.2025.15728381572838Artificial intelligence-assisted early screening of acute promyelocytic leukaemia in blood smears: a prospective evaluation of MC-100iFan Zhang0Pingjuan Liu1Jieyu Zhan2Jing Cheng3Hongxia Tan4Jiahang Zhang5Meiqi Song6Fengying Wu7Qiuyi Lin8Zhuangbiao Shi9Chanjun Yang10Meinan Wang11Qiu Li12Yang Wang13Liubing Li14Junxun Li15Department of Medical Laboratory, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, ChinaDepartment of Medical Laboratory, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, ChinaDepartment of Pediatric, Baiyun District Maternal and Child Healthcare Centre, Guangzhou, ChinaDepartment of Medical Laboratory, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, ChinaDepartment of Medical Laboratory, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, ChinaSchool of Medical Technology, Guangdong Medical University, Dongguan, ChinaSchool of Medical Technology, Guangdong Medical University, Dongguan, ChinaYunkang School of Medicine and Health, Nanfang College, Guangzhou, ChinaYunkang School of Medicine and Health, Nanfang College, Guangzhou, ChinaYunkang School of Medicine and Health, Nanfang College, Guangzhou, ChinaDepartment of Blood Transfusion, The Second Affiliated Hospital of Shantou University Medical College, Shantou, ChinaIVD Domestic Clinical Application Department, Mindray Biomedical Electronics Co., Ltd., Shenzhen, Guangdong, ChinaSchool of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, ChinaDepartment of Medical Laboratory, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, ChinaDepartment of Medical Laboratory, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, ChinaDepartment of Medical Laboratory, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, ChinaObjectivesIdentification 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.MethodsOne 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.ResultsThe 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.ConclusionsMC-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.https://www.frontiersin.org/articles/10.3389/fonc.2025.1572838/fullacute promyelocytic leukaemiaMC-100iearly screeningartificial intelligencemorphologyblood smears |
| spellingShingle | Fan Zhang Pingjuan Liu Jieyu Zhan Jing Cheng Hongxia Tan Jiahang Zhang Meiqi Song Fengying Wu Qiuyi Lin Zhuangbiao Shi Chanjun Yang Meinan Wang Qiu Li Yang Wang Liubing Li Junxun Li Artificial intelligence-assisted early screening of acute promyelocytic leukaemia in blood smears: a prospective evaluation of MC-100i Frontiers in Oncology acute promyelocytic leukaemia MC-100i early screening artificial intelligence morphology blood smears |
| title | Artificial intelligence-assisted early screening of acute promyelocytic leukaemia in blood smears: a prospective evaluation of MC-100i |
| title_full | Artificial intelligence-assisted early screening of acute promyelocytic leukaemia in blood smears: a prospective evaluation of MC-100i |
| title_fullStr | Artificial intelligence-assisted early screening of acute promyelocytic leukaemia in blood smears: a prospective evaluation of MC-100i |
| title_full_unstemmed | Artificial intelligence-assisted early screening of acute promyelocytic leukaemia in blood smears: a prospective evaluation of MC-100i |
| title_short | Artificial intelligence-assisted early screening of acute promyelocytic leukaemia in blood smears: a prospective evaluation of MC-100i |
| title_sort | artificial intelligence assisted early screening of acute promyelocytic leukaemia in blood smears a prospective evaluation of mc 100i |
| topic | acute promyelocytic leukaemia MC-100i early screening artificial intelligence morphology blood smears |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1572838/full |
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