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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT05963945
Other study ID # 00002
Secondary ID
Status Completed
Phase
First received
Last updated
Start date October 18, 2022
Est. completion date March 21, 2023

Study information

Verified date July 2023
Source Carebot s.r.o.
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

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


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).


Recruitment information / eligibility

Status Completed
Enrollment 956
Est. completion date March 21, 2023
Est. primary completion date November 17, 2022
Accepts healthy volunteers
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria: - Hospital patients > 18 years who were referred for chest radiography October 18th, 2022, and November 17th, 2022 at the Radiodiagnostic Department of the Havírov Hospital, p.o. Exclusion Criteria: - Patients < 18 years - Chest X-ray images in lateral positions - Duplicated chest X-ray images

Study Design


Intervention

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.

Locations

Country Name City State
Czechia Nemocnice Havírov, p. o. Havírov

Sponsors (1)

Lead Sponsor Collaborator
Carebot s.r.o.

Country where clinical trial is conducted

Czechia, 

References & Publications (1)

KVAK, Daniel, Anna CHROMCOVÁ, Petra OVESNÁ, Jakub DANDÁR, Marek BIROŠ, Robert HRUBÝ, Daniel DUFEK a Marija PAJDAKOVIC. Can Deep Learning Reliably Recognize Abnormality Patterns on Chest X-rays? A Multi-Reader Study Examining One Month of AI Implementation

Outcome

Type Measure Description Time frame Safety issue
Primary Performance test The primary objective is to evaluate the performance parameters of the proposed DLAD (Carebot AI CXR) in comparison to individual radiologists. The performance test includes sensitivity and specificity, positive and negative likelihood ratio, and positive and negative predictive value. The aforementioned parameters are statistically compared using confidence intervals (CI) and p-Values. The comparison procedure consists of two steps: a global hypothesis test is conducted to determine whether there are significant differences between DLAD and radiologists. If the global hypothesis test yields a significant result, individual hypothesis tests are performed. Additionally, multiple comparison methods, such as McNemar with continuity correction for Se and Sp, Holm method for LRs, and weighted generalized score statistics for PVs, are applied to control the overall error rate. All tests are performed as two-tailed tests at the 5% significance level. March 2023
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