Clinical validation pipeline of a deep learning model for segmenting and quantifying intracranial and ventricular volumes on computed tomography

In the evolving landscape of radiology, the demand for advanced medical imaging techniques, particularly computed tomography (CT), continues to rise. However, the interpretation of these scans heavily relies on the availability of expert radiologists, a challenge particularly prevalent in resource-limited areas. Recent advancements in artificial intelligence (AI) and deep learning (DL) have shown promise in assisting radiologists with image interpretation and diagnosis.

This study centers on validating DeepCTE3D, a deep learning-based model utilizing 3D architecture for segmenting and quantifying intracranial volume (ICV) and lateral ventricular volume (LVV) in CT scans. The model's efficacy was assessed using a dataset featuring diverse patient demographics and various scanner models, encompassing both normal and pathological scans.

To evaluate the model, a streamlined pipeline was developed to generate ground-truth results, which were then compared to the model's outputs. The findings demonstrated high similarity scores for both ICV and LVV. Secondary analyses yielded differences in LVV and ICV based on patient sexes and scanner models; however, these discrepancies were deemed clinically insignificant.

This research underscores the potential of DeepCTE3D in enhancing clinical triage and advancing neuroimaging applications, especially in situations where MRI is not feasible. The growing accessibility of noninvasive imaging techniques like MRI and CT has heightened the demand for these services. Their extensive use for routine check-ups and early detection strategies has driven the need for radiologists to interpret these exams.

This demand is further compounded by the shortage of radiologists, especially in resource-limited regions, which hinders timely diagnosis. Recent AI and DL advancements present promising solutions.

Unlike traditional machine learning, which relies on predefined features, DL employs artificial neural networks capable of automatically identifying complex patterns from extensive image datasets. This eliminates the need for manual feature engineering and enables models to capture nuanced details, thereby improving accuracy in tasks such as segmentation, classification, and anomaly detection.

DL's versatility shines in various radiology workflow tasks, including image abnormality detection, patient triage, and expedited treatment of potentially lethal or debilitating conditions.

In neuroradiology, AI algorithms can expedite CT scan reading by automatically detecting image abnormalities and critical findings like ischemic stroke and intracranial hemorrhage. By automating time-consuming brain measurements and segmenting brain regions, DL provides neuroradiologists with quantitative tools for detecting and monitoring diseases.

Intracranial volume (ICV) and lateral ventricular volume (LVV) are two essential measures in this context. ICV represents the total space occupied by the brain, its protective membranes, and cerebrospinal fluid within the skull, excluding the spinal cord. LVV, corresponding to the summed volume of the lateral ventricles, holds significance in diagnosing and monitoring various diseases, particularly neurodegenerative conditions and hydrocephalus.

Quantifying LVV offers objective data for diagnosis, enabling physicians to assess and monitor abnormality degrees.