NCT05963945

Multi-Reader Retrospective Study Examining Carebot AI CXR 2.0.21-v2.01 Implementation in Everyday Radiology Clinical Practice

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 956 (actual)
• Sponsor: Carebot s.r.o. · Industry
• Sex: All
• Age: 18 Years
• Healthy volunteers: —

Summary

The primary objective is to evaluate the performance parameters of the proposed DLAD (Carebot AI CXR) in comparison to individual radiologists.

Detailed description

In the period between October 18th, 2022, and November 17th, 2022, anonymized chest X-ray images of patients were collected at the Radiodiagnostic Department of the Havířov Hospital, p.o. The collection process involved utilizing the CloudPACS imaging and archiving system provided by OR-CZ spol. s r.o. The collected X-ray images were subjected to the proposed DLAD (Carebot AI CXR) for analysis. Subsequently, the DLAD's performance was compared with the standard clinical practice, where radiologists assessed the CXR images in the simulated hospital setting with access to standard viewing tools (e.g., pan, zoom, WW/WL) and were given an unlimited amount of time to complete the review. Each radiologist determined the presence or absence of 7 indicated radiological findings, including atelectasis (ATE), consolidation (CON), pleural effusion (EFF), pulmonary lesion (LES), subcutaneous emphysema (SCE), cardiomegaly (CMG), and pneumothorax (PNO).

Conditions

• Pneumothorax
• Pulmonary Nodule, Solitary
• Atelectasis
• Subcutaneous Emphysema
• Cardiomegaly
• Consolidation
• Pleural Effusion

Interventions

Type	Name	Description
DEVICE	Carebot AI CXR	The proposed DLAD (Carebot AI CXR) is a deep learning-based medical device designed to assist radiologists in interpreting chest X-ray images acquired in anteroposterior (AP) or posteroanterior (PA) projection. By employing advanced deep learning algorithms, this solution enables automatic detection of abnormal findings by analyzing visual patterns associated with specific conditions. The targeted abnormalities include atelectasis (ATE), consolidation (CON), pleural effusion (EFF), pulmonary lesion (LES), subcutaneous emphysema (SCE), cardiomegaly (CMG), and pneumothorax (PNO). The DLAD functions as a prediction algorithm complemented by various application peripherals, such as web-based communication tools, DICOM file conversion capabilities, and storage and reporting libraries supporting both DICOM Structured Report and DICOM Presentation State formats.

Timeline

• Start date: 2022-10-18
• Primary completion: 2022-11-17
• Completion: 2023-03-21
• First posted: 2023-07-27
• Last updated: 2023-07-27

Locations

1 site across 1 country: Czechia