Reshaping CT imaging workflow through intelligent AI orchestration using RAPID: radiologic automated processing for image distribution
Author Block: R. Hosch, V. Parmar, J. Kohnke, K. A. Borys, K. Arzideh, G. Baldini, J. Haubold, L. Umutlu, F. Nensa; Essen/DE
Purpose: The purpose of this study was to introduce RAPID, an algorithm for swiftly and automatically orchestrating images based on detected anatomical landmarks and body regions.In the rapidly evolving medical AI field, radiologists are incorporating AI models into clinical practice, aiming for enhanced efficiency and workflow optimisation. This necessitates the implementation of an "Orchestrator" capable of automatically directing images to appropriate AI models without manual intervention. Existing CT solutions predominantly rely on DICOM tags, which offer limited and often unreliable information like SeriesDescription.
Methods or Background: 13,211 abdominal and 6,789 whole-body CT scans from 20,000 patients (42.75% female) were used. Topograms from these scans were employed for three tasks: classification (torso, head-neck, hands, legs), region detection (head, brain, pericardium, thorax, abdomen), and organ detection (lung, heart, spine, liver, kidneys, spleen, stomach, colon, pancreas, brain, hip). Series specific organ and body region segmentations generated using the Body and Organ Analysis algorithm (BOA) were mapped onto topograms using DICOM geometry as ground truth. YOLOv8 models were trained for classification and object detection and evaluated using F1-score and mean Average Precision (mAP0.5).
Results or Findings: Classification achieved a weighted F1-score of 0.92. Region detection reached 0.96 mAP, while organ detection scored 0.94 mAP. After topogram-based robust classification and detection, orchestration rules were established to automatically route series to suitable AI models if they met the model’s prerequisites.
Conclusion: RAPID accurately and efficiently locates body regions and organs in CT scans using topograms. These landmarks facilitate series orchestration for AI applications. RAPID employs "deep content inspection" for precise routing decisions, prioritizing image data over manual entered meta data.
Limitations: The trained models should be evaluated on external datasets. In addition, the number of relevant landmarks for object detection should be extended.
Funding for this study: No funding was received for this study.
Has your study been approved by an ethics committee? Yes
Ethics committee - additional information: This study adhered to all guidelines defined by the approving institutional review board of the investigating hospital. The Institutional Review Board waived written informed consent due to the study's retrospective nature. Complete anonymisation of all data was performed before inclusion in the study.