Clinical trial NCT05594485: Retrospective Study of Carebot AI CXR Performance in Preclinical Practice
Official title: Chest X-Ray Abnormality Detection Using Artificial Intelligence: Retrospective Study of Carebot AI CXR Performance in Preclinical Practice
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Artificial Intelligence
Lung Diseases
Pneumothorax
Pleural Effusion
Cardiomegaly
Lung Cancer
Pulmonary Edema
Consolidation
Pneumonia
Atelectasis
Hilar Calcification
Fracture Rib
Lead sponsor: Carebot s.r.o.
Sponsor class: INDUSTRY
First posted: 2022-10-26
Enrollment: 127
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: 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.

Brief summary: 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: 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.

Eligibility criteria: 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

Primary outcomes:
measure: Primary objective
timeFrame: 20-10-2022
Secondary outcomes:
measure: Secondary objective
timeFrame: 20-10-2022
Other outcomes:
Locations:
city: Havířov
country: Czechia
facility: Nemocnice Havířov, p. o.
Study officials:
References: