Table 6.

Clinical trials about AI in ultrasound.

Study	ClinicalTrials.gov identifier	Office title	Country	Study type	No. of patients	Conditions	Intervention	Primary outcome measurement	Summary
1	NCT04876157	Artificial Intelligence-aimed Point-of-care Ultrasound Image Interpretation System	China	Interventional	300	Ultrasound Image Interpretation	Diagnostic Test: Artificial intelligence-aimed point-of-care ultrasound image interpretation system	Sensitivity and specificity of AI interpretation	The main project is responsible for coordination between the two sub-projects and the main project, providing image resources, and using U-Net (Convolutional Networks for Biomedical Image Segmentation) and Transfer Learning to build up the models for image recognition and validating the efficacy of the models.
2	NCT05151939	Endoscopic Ultrasound (EUS) Artificial Intelligence Model for Normal Mediastinal and Abdominal Strictures Assessment	Ecuador	Observational	60	Abdomen; Mediastinum; Anatomic; Abnormality; Strictures	Diagnostic Test: Identification or discharge visualization of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos by an expert endoscopist Diagnostic Test: Recognition of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos using artificial intelligence (AI)	Overall accuracy of Endoscopic ultrasound (EUS) artificial intelligence (AI) model for identifying normal mediastinal and abdominal organ/anatomic strictures	Artificial intelligence (AI) aided recognition of anatomical structures may improve the training process and inter-observer agreement.
3	NCT04580095	Artificial Intelligence for Improved Echocardiography	Norway	Interventional	80	Heart Diseases	AI algorithm for apical foreshortening in echocardiography	Left ventricular apical foreshortening	The purpose of this study is to assess the effect of artificial intelligence algorithms on image quality in echocardiography.
4	NCT03849040	The Use of Artificial Intelligence to Predict Cancerous Lymph Nodes for Lung Cancer Staging During Ultrasound Imaging	Canada	Observational	52	Lung Diseases; Lung Neoplasm	Procedure: Endobronchial Ultrasound	Development of computer algorithm to identify lymph node ultrasonographic features Validation of computer algorithm to identify lymph node ultrasonographic features Accuracy and reliability of the segmentation performed by NeuralSeg NeuralSeg prediction of lymph node malignancy	This study aims to determine if a deep neural artificial intelligence (AI) network (NeuralSeg) can learn how to assign the Canada Lymph Node Score to lymph nodes examined by endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA), using the technique of segmentation.
5	NCT04270032	Using Deep Learning Methods to Analyze Automated Breast Ultrasound Images, to Establish a Diagnosis, Therapy Assessment and Prognosis Prediction Model of Breast Cancer.	China	Observational	10,000	Breast Cancer	Diagnostic Test: ABUS	Sensitivity false-positive per volume area under curve	The purpose of this study is using a deep learning method to analyze the automated breast ultrasound (ABUS) imagings, establish and evaluate a diagnosis, therapy assessment and prognosis prediction model of breast cancer.