A Novel Deep Learning-based Artificial Intelligence System for Interpreting Urolithiasis in Computed Tomography

Our aim was to develop an artificial intelligence (AI) system for detection of urolithiasis in computed tomography (CT) images using advanced deep learning capable of real-time calculation of stone parameters such as volume and density, which are essential for treatment decisions. The performance of the system was compared to that of urologists in emergency room (ER) scenarios.

Axial CT images for patients who underwent stone surgery between August 2022 and July 2023 comprised the data set, which was divided into 70% for training, 10% for internal validation, and 20% for testing. Two urologists and an AI specialist annotated stones using Labelimg for ground-truth data. The YOLOv4 architecture was used for training, with acceleration via an RTX 4900 graphics processing unit (GPU). External validation was performed using CT images for 100 patients with suspected urolithiasis.

The AI system was trained on 39 433 CT images, of which 9.1% were positive. The system achieved accuracy of 95%, peaking with a 1:2 positive-to-negative sample ratio. In a validation set of 5736 images (482 positive), accuracy remained at 95%. Misses (2.6%) were mainly irregular stones. False positives (3.4%) were often due to artifacts or calcifications. External validation using 100 CT images from the ER revealed accuracy of 94%; cases that were missed were mostly ureterovesical junction stones, which were not included in the training set. The AI system surpassed human specialists in speed, analyzing 150 CT images in 13 s, versus 38.6 s for evaluation by urologists and 23 h for formal reading. The AI system calculated stone volume in 0.2 s, versus 77 s for calculation by urologists.

Our AI system, which uses advanced deep learning, assists in diagnosing urolithiasis with 94% accuracy in real clinical settings and has potential for rapid diagnosis using standard consumer-grade GPUs.

We developed a new AI (artificial intelligence) system that can quickly and accurately detect kidney stones in CT (computed tomography) scans. Testing showed that this system is highly effective, with accuracy of 94% for real cases in the emergency department. It is much faster than traditional methods and provides rapid and reliable results to help doctors in making better treatment decisions for their patients.