FAD-YOLO: a lightweight feature-refined and task-aligned framework for AIS–MIA discrimination on pulmonary CT

Category	Detail
Condition	Adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA)
Key Mechanisms	Automated detection using deep learning frameworks, specifically FAD-YOLO.
Target Population	Patients with ground-glass nodules (GGNs) on pulmonary CT imaging.
Care Setting	Resource-constrained clinical devices for lung cancer diagnosis.

FAD-YOLO achieved a precision of 93.8% and recall of 93.4% on an independent test set.
The model reduces parameters by 18.7% compared to the baseline while increasing GFLOPs by 12.5%.
Outperformed larger models like RT-DETR-R50 on accuracy metrics despite fewer parameters.
Demonstrated cross-dataset generalization without fine-tuning on external test sets.
Addresses challenges in automated discrimination of AIS and MIA due to morphological variability.

Consider accurate imaging-based discrimination for surgical planning and treatment decisions.

Regular assessment of model performance on diverse datasets to ensure reliability.

Patients presenting with ground-glass nodules on CT scans.

Accurate classification can guide appropriate surgical interventions.

Integrate FAD-YOLO into routine imaging workflows for lung cancer assessment.
Ensure continuous validation of the model's performance across different clinical settings.

Clinical Scorecard: FAD-YOLO: An Efficient Framework for Distinguishing AIS from MIA in Pulmonary CT Imaging