A Deep Learning Model for IMMP-Based Residual Disease Monitoring in AML with Monocytic Differentiation
This deep learning model provides an automated, objective tool for quantifying immature monocytes, addressing key limitations in morphological assessment of monocytic AML. The IMMP metric shows promise for monitoring treatment response, predicting relapse, and potentially identifying patients at risk of venetoclax-based therapy resistance. While promising, prospective multicenter validation is needed to translate these findings into routine clinical practice.
Authors
- Huidan Li (ORCID: https://orcid.org/0000-0002-8293-1903)
- Jiatao Lou (ORCID: https://orcid.org/0000-0001-8191-3255)
- Chunling Zhang (ORCID: https://orcid.org/0000-0002-9311-0000)
- W.T. Liu (ORCID: https://orcid.org/0000-0003-4885-1675)
- Zongyue Lu
- Ting Xu
- Huiying Qiu
- Xinyi Jin (ORCID: https://orcid.org/0000-0003-0227-1368)
- Jing Ding (ORCID: https://orcid.org/0009-0006-0487-7821)
Institutions
- Shanghai Jiao Tong University (CN)
- Focused Photonics (China) (CN)
- Zhejiang Lab (CN)
- Shanghai First People's Hospital (CN)
- Shanghai Children's Hospital (CN)
Publication Details
- Journal
- Diagnostics
- Published
- 2026-04-21
- DOI
- https://doi.org/10.3390/diagnostics16081244
- Primary Topic
- Digital Imaging for Blood Diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.00