■肾积水对肾绞痛的诊断至关重要。我们从超声图像中自动检测肾积水,以规范治疗并减少肾绞痛的误诊。
■匿名收集人体肾脏的超声图像,正常和肾积水,对神经网络进行了预处理。对六个“最先进的”模型进行了训练和交叉验证,用于检测肾积水,两个卷积网络用于肾脏分割。在测试阶段,性能指标包括真阳性,真正的底片,假阳性,假阴性,准确度,和F1得分,虽然分割任务的评估涉及准确性,精度,骰子,Jaccard,召回,ASSD。
■从三种不同的超声设备收集了总共523张超声肾脏图像(423张非肾积水和100张非肾积水)。在这个数据集上训练后,所有模型均用于评估200例新的超声肾脏图像(142例非肾积水肾脏和58例肾积水肾脏).AlexNet模型(GoogLeNet97%,AlexNet_v296%,ResNet5096%,ResNet10197.5%,和ResNet15295%)。deeplabv3_resnet50和deeplabv3_resnet101的骰子系数分别为94.74%和94.48%,分别,关于自动肾脏分割的任务。这项研究仅限于分析肾积水,但是这种特定的焦点能够实现高检测精度。
■我们表明,我们的自动超声深度学习模型可以进行训练,并用于以高精度解释和分割来自不同来源的超声图像。该方法将作为未来急性肾衰竭诊断算法的自动化工具。
■肾积水对诊断肾绞痛至关重要。人工智能的最新进展允许高精度地自动检测超声图像中的肾积水。这些方法将有助于规范诊断和治疗肾绞痛。
UNASSIGNED: Hydronephrosis is essential in the diagnosis of renal colic. We automated the detection of hydronephrosis from ultrasound images to standardize the therapy and reduce the misdiagnosis of renal colic.
UNASSIGNED: Anonymously collected ultrasound images of human kidneys, both normal and hydronephrotic, were preprocessed for neural networks. Six \"state of the art\" models were trained and cross-validated for the detection of hydronephrosis, and two convolutional networks were used for kidney segmentation. In the testing phase, performance metrics included true positives, true negatives, false positives, false negatives, accuracy, and F1 score, while the evaluation of the segmentation task involved accuracy, precision, dice, jaccard, recall, and ASSD.
UNASSIGNED: A total of 523 sonographic kidney images (423 nonhydronephrotic and 100 hydronephrotic) were collected from three different ultrasound devices. After training on this dataset, all models were used to evaluate 200 new ultrasound kidney images (142 nonhydronephrotic and 58 hydronephrotic kidneys). The highest validation accuracy (98.5%) was achieved by the AlexNet model (GoogLeNet 97%, AlexNet_v2 96%, ResNet50 96%, ResNet101 97.5%, and ResNet152 95%). The deeplabv3_resnet50 and deeplabv3_resnet101 reached a dice coefficient of 94.74% and 94.48%, respectively, on the task of automated kidney segmentation. The study is limited by analyzing only hydronephrosis, but this specific focus enabled high detection accuracy.
UNASSIGNED: We show that our automated ultrasound deep learning model can be trained and used to interpret and segmentate ultrasound images from different sources with high accuracy. This method will serve as an automated tool in the diagnostic algorithm of acute renal failure in the future.
UNASSIGNED: Hydronephrosis is crucial in the diagnosis of renal colic. Recent advances in artificial intelligence allow automated detection of hydronephrosis in ultrasound images with high accuracy. These methods will help standardize the diagnosis and treatment renal colic.