A universally accessible residual Network–Assisted diagnostic and analytic tool (RADIANT) for acute lymphoblastic leukemia Free

Background: Acute lymphoblastic leukemia (ALL) remains the predominant pediatric malignancy and poses significant therapeutic challenges in adults as well. Although genomic and molecular diagnostics have refined risk stratification, morphologic assessment of leukemic blasts continues to depend on expert hematopathologists, introducing inter-observer variability and diagnostic latency. Convolutional neural networks (CNNs), particularly deep learning residual network architectures, have emerged as potent tools to automate cytomorphological evaluation, offering reproducible and accurate classification. We present the development and independent validation of a ResNet34-based algorithm, embedded within a secure, cross-platform application, for the rapid and accurate classification of ALL.

Methodology: A state-of-the-art residual network architecture (ResNet34) was trained on an anonymized dataset of hematoxylin and eosin-stained peripheral blood smear images, apportioned in the ratio 3:1:1 into training, validation, and testing cohorts, respectively. To ensure generalizability and mitigate overfitting, we employed sophisticated data augmentation techniques during training. Final evaluation was conducted on the testing cohort, with expert physician-scientists confirming ground-truth labels.

Results: On the testing cohort (n = 2,436), ResNet34 attained an overall accuracy of 99.96% (2,435/2,436). Class-specific accuracies were 100.0% for benign lymphocytes (372/372), early-stage pre-B ALL blasts (750/750), and pro-B ALL blasts (607/607), and 99.9% for pre-B ALL blasts (706/707). The sole error comprised a pre-B blast misclassified as early-stage pre-B. Receiver operating characteristic curves yielded areas under the curve of 1.00 across all four categories, demonstrating flawless discrimination. Precision–recall analyses likewise produced average precision of 1.00 for each class, confirming the absence of false-positive classifications. Training and validation accuracy converged to ≥99.9% by epoch 5, with training and validation losses approaching zero and only transient validation loss fluctuations (< 0.2) at later epochs. Inference averaged less than a second per image, affirming suitability for real-time clinical workflows.

Conclusions: The ResNet34-powered application demonstrates near-perfect accuracy in distinguishing ALL subtypes and benign lymphocytes, matching or exceeding expert hematopathologist performance with remarkable consistency. The solitary misclassification highlights a negligible specificity gap, which can be further minimized by incorporating adjunctive cytogenetic or molecular data. Rapid convergence and minimal loss fluctuations underscore the model's robustness and generalizability. Collectively, these findings support the deployment of residual network architecture-assisted diagnostic and analytical tools as a boon to hematopathology practice. Thus, artificial intelligence can enable sub-second, reproducible classification that can streamline diagnostics, facilitate tele-hematology, and democratize access to expert-level interpretation, particularly in resource-constrained environments. Future efforts will focus on prospective, multi-institutional validation and integration of multimodal datasets to enhance prognostic stratification and guide precision-medicine interventions in hematology.