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TRUVACE RECORD VERSION
record: TRV-2026-1264
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-03T06:56:19.073493Z
status: published
lens: g_space
sector: health
headline: Person-Based Progression in Acute Myeloid Leukemia Classification: A Multi-View Convolutional Neural Network Approach
dek: Acute myeloid leukemia (AML) is a hematologic malignancy in which accurate subtype identification is important for treatment planning and prognosis. This study developed a deep learning approach for AML subtype classification using multiple single-cell images from each patient rather than conventional instance-based classification. An open-source dataset containing 81,214 single-cell images from 189 individuals was analyzed. The dataset included four genetically defined AML subtypes and a healthy control group.…
gain_title: Patient-level 500-view convolutional neural network improved acute myeloid leukemia subtype classification, achieving 0.8783 accuracy and 0.8622 F1 score compared to single-view analysis.
problem_title: (none)
trace_subject: (none)
gain_reading: Patient-level 500-view convolutional neural network improved acute myeloid leukemia subtype classification, achieving 0.8783 accuracy and 0.8622 F1 score compared to single-view analysis.
gain_evidence: The 500-view MV-CNN achieved the best performance, with an accuracy of 0.8783, F1 score of 0.8622, sensitivity of 0.8637, specificity of 0.8774, precision of 0.8616, and MCC of 0.8441. | Patient-level multi-view analysis improved AML subtype classification compared with single-image analysis
problem_reading: (none)
problem_evidence: (none)
quick_read: By the publication date of 2026-10-01, researchers had developed and evaluated a person-based multi-view CNN approach for AML subtype classification using 81,214 single-cell images from 189 individuals. The 500-view MV-CNN achieved accuracy 0.8783, F1 0.8622, sensitivity 0.8637, specificity 0.8774, precision 0.8616 and MCC 0.8441, outperforming a single-view CNN baseline in tenfold cross-validation.

The result matters because accurate subtype identification is important for treatment planning and prognosis in AML, and patient-level aggregation of multiple cell images improved classification over conventional instance-based analysis. What remains uncertain is clinical translation, as the source describes performance on an open-source dataset and supports potential for hematologic image classification rather than reporting prospective clinical deployment or patient outcomes.
limitation: 
tag: Evidence-backed gain
key_points: Study used open-source dataset of 81,214 single-cell images from 189 individuals covering four genetically defined AML subtypes and healthy controls. | Two MV-CNN models were tested: 99-view model based on random sampling and 500-view model using image augmentation when fewer than 500 images were available per patient. | Performance assessed by tenfold cross-validation on accuracy, F1, sensitivity, specificity, precision, and MCC; single-view CNN yielded the lowest values.
rundown: The comparison was a single-view CNN versus two multi-view approaches, with the 99-view model showing lower but comparable performance to the 500-view model.

Dataset included four genetically defined AML subtypes plus a healthy control group, and evaluation used tenfold cross-validation across six metrics including Matthews correlation coefficient.
sources:
- peer_reviewed | Journal of Imaging Informatics in Medicine | https://doi.org/10.1007/s10278-026-02371-7 | 2026-10-01
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