Clinical Validation of a Deep Learning Model forSegmenting and Quantifying Intracranial andVentricular Volumes on Computed Tomography

The field of radiology is experiencing a surge in demand due to advances in medical imaging, particularly in techniques suchas magnetic resonance imaging and computed tomography (CT). However, the interpretation of these scans relies heavilyon the availability of experts, which is challenging in resource-limited regions. Recent advances in artificial intelligence anddeep learning offer promising solutions by assisting radiologists in image interpretation and diagnosis. This study focuses onvalidating DeepCTE3D, a deep learning–based model for segmenting and quantifying intracranial volume (ICV) and lateralventricular volume (LVV) in CT scans. The model’s performance was evaluated using a real-world dataset comprising diversepatient demographics and various scanner models, including normal and pathological scans. The evaluation process involveddeveloping a streamlined pipeline to generate ground-truth results and comparing them to the model’s outputs. DeepCTE3Dachieved high similarity scores for both ICV and LVV. Secondary analyses revealed differences in LVV and ICV between patientsexes and scanner models, although these differences were probably not clinically significant. This study highlights the potentialof DeepCTE3D in enhancing clinical triage and advancing neuroimaging applications, especially in scenarios where MRI is notfeasible.