Abstract：
OBJECTIVE: 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.
METHODS: 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.
UNASSIGNED: 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.
CONCLUSIONS: 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.
RESULTS: 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.
METHODS: 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.
UNASSIGNED: 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.
CONCLUSIONS: 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.
RESULTS: 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.
摘要：
目标：我们的目标是开发一种人工智能（AI）系统，用于使用先进的深度学习来检测计算机断层扫描（CT）图像中的尿路结石，能够实时计算结石参数，例如体积和密度。这对治疗决策至关重要。将系统的性能与急诊室（ER）场景中泌尿科医生的性能进行了比较。
方法：2022年8月至2023年7月接受结石手术的患者的轴向CT图像包括数据集，分成70%用于培训，10%用于内部验证，20%用于测试。两名泌尿科医生和一名AI专家使用Labelig为地面数据注释了石头。YOLOv4架构用于培训，通过RTX4900图形处理单元(GPU)加速。使用CT图像对100例可疑尿石症患者进行外部验证。
■AI系统在39.433个CT图像上进行了训练，其中9.1%为正。该系统实现了95%的准确度,以1：2的正负样本比率达到峰值。在5736张图像的验证集中（482张阳性），准确率保持在95%。遗漏（2.6％）主要是不规则结石。假阳性（3.4％）通常是由于伪影或钙化。使用来自ER的100张CT图像进行的外部验证显示准确率为94％；错过的病例大多是输尿管膀胱交界处结石，不包括在训练集中。人工智能系统在速度上超过了人类专家，在13秒内分析150张CT图像，而泌尿科医师评估为38.6s，正式阅读为23h。AI系统在0.2s内计算石头体积，与泌尿科医生计算的77秒相比。
结论：我们的人工智能系统，它使用先进的深度学习，在真实临床环境中协助诊断尿石症的准确率为94%,并具有使用标准消费级GPU进行快速诊断的潜力。
结果：我们开发了一种新的AI（人工智能）系统，该系统可以在CT（计算机断层扫描）扫描中快速准确地检测肾结石。测试表明，该系统非常有效，急诊科真实病例的准确率为94%。它比传统方法快得多，并提供快速可靠的结果，以帮助医生为患者做出更好的治疗决策。
方法：2022年8月至2023年7月接受结石手术的患者的轴向CT图像包括数据集，分成70%用于培训，10%用于内部验证，20%用于测试。两名泌尿科医生和一名AI专家使用Labelig为地面数据注释了石头。YOLOv4架构用于培训，通过RTX4900图形处理单元(GPU)加速。使用CT图像对100例可疑尿石症患者进行外部验证。
■AI系统在39.433个CT图像上进行了训练，其中9.1%为正。该系统实现了95%的准确度,以1：2的正负样本比率达到峰值。在5736张图像的验证集中（482张阳性），准确率保持在95%。遗漏（2.6％）主要是不规则结石。假阳性（3.4％）通常是由于伪影或钙化。使用来自ER的100张CT图像进行的外部验证显示准确率为94％；错过的病例大多是输尿管膀胱交界处结石，不包括在训练集中。人工智能系统在速度上超过了人类专家，在13秒内分析150张CT图像，而泌尿科医师评估为38.6s，正式阅读为23h。AI系统在0.2s内计算石头体积，与泌尿科医生计算的77秒相比。
结论：我们的人工智能系统，它使用先进的深度学习，在真实临床环境中协助诊断尿石症的准确率为94%,并具有使用标准消费级GPU进行快速诊断的潜力。
结果：我们开发了一种新的AI（人工智能）系统，该系统可以在CT（计算机断层扫描）扫描中快速准确地检测肾结石。测试表明，该系统非常有效，急诊科真实病例的准确率为94%。它比传统方法快得多，并提供快速可靠的结果，以帮助医生为患者做出更好的治疗决策。
