Retrospective Study of Carebot AI CXR Performance in Preclinical Practice

Study Overview
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Brief Summary
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The purpose of this study is to describe the design, methodology and evaluation of the preclinical test of Carebot AI CXR software, and to provide evidence that the investigated medical device meets user requirements in accordance with its intended use. Carebot AI CXR is defined as a recommendation system (classification prediction) based on computer-aided detection. The software can be used in a preclinical deployment at a selected site before interpretation (prioritization, display of all results and heatmaps) or after interpretation (verification of findings) of CXR images, and in accordance with the manufacturer's recommendations. Given this, a retrospective study is performed to test the clinical effectiveness on existing CXRs.

Detailed Description
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The performance of the trained and internally validated Carebot AI CXR software is tested on a set of 127 CXR images from target population. This is compared to common clinical practice, i.e., image assessment by a radiologist in a hospital. Patients may have a variety of findings; at this stage of the evaluation, an abnormal finding is considered to be an abnormality in any of the defined classes. False negative images incorrectly predicted by Carebot AI CXR software result in a clinical impact determination. To collect the CXR data for retrospective study, investigators addressed a municipal hospital in the Czech Republic that provides healthcare services to up to 130,000 residents of a medium-sized city (approximately 70,000 inhabitants) and the surrounding area. 127 anonymized CXR images were collected between August 15 and 17, 2022, and subsequently submitted to five independent radiologists of varying experience for annotation. The selected radiologists were asked to assess whether the CXR image shows any of the 12 pre-selected abnormalities. Pediatric CXR images (under 18 years of age), scans with technical problems (poor image quality, rotation), and images in lateral projection were excluded from the dataset.

Official Title
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Chest X-Ray Abnormality Detection Using Artificial Intelligence: Retrospective Study of Carebot AI CXR Performance in Preclinical Practice

Conditions
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Artificial Intelligence, Lung Diseases, Pneumothorax, Pleural Effusion, Cardiomegaly, Lung Cancer, Pulmonary Edema, Consolidation, Pneumonia, Atelectasis, Hilar Calcification, Fracture Rib

Intervention / Treatment
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* Device: Carebot AI CXR


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Hospital patients who were referred for chest radiography between August 15 and 17, 2022. Exclusion Criteria: Pediatric CXR images (under 18 years of age) Scans with technical problems (poor image quality, rotation) Images in lateral projection

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Retrospective collection of DICOM patient files for the period 15-17 August<br>To collect the CXR data for retrospective study, we addressed a municipal hospital in the Czech Republic that provides healthcare services to up to 130,000 residents of a medium-sized city (approximately 70,000 inhabitants) and the surrounding area. 127 anonymized CXR images were collected between August 15 and 17, 2022, and subsequently submitted to five independent radiologists of varying experience for annotation. The selected radiologists were asked to assess whether the CXR image shows any of the 12 abnormalities mentioned above. Pediatric CXR images (under 18 years of age), scans with technical problems (poor image quality, rotation), and images in lateral projection were excluded from the dataset. | Device: Carebot AI CXR<br>* Carebot AI CXR is a deep learning-based software that assists radiologists in the interpretation of chest radiographs in posterior-anterior (PA) or anterior-posterior (AP) projections. The solution with artificial intelligence automatically detects abnormality based on visual patterns for the following findings: atelectasis, consolidation, cardiomegaly, mediastinal widening, pneumoperitoneum, pneumothorax, pulmonary edema, pulmonary lesion, bone fracture, hilar enlargement, subcutaneous emphysema, and pleural effusion.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Primary objective | Comparison of the accuracy of radiologist and Carebot AI CXR image assessment. | 20-10-2022 | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Secondary objective | Comparison of the accuracy of radiologis with different experience vs. Carebot AI CXR. Weakness assessment of Carebot AI CXR. | 20-10-2022 |