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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.

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.

Carebot AI CXR

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.

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.

Retrospective Study of Carebot AI CXR Performance in Preclinical Practice

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

The results of the retrospective study are used to validate the Carebot AI CXR software for the target population prior to deployment in the workplace.

Comparison of the accuracy of radiologist and Carebot AI CXR image assessment.

Comparison of the accuracy of radiologis with different experience vs. Carebot AI CXR. Weakness assessment of Carebot AI CXR.

Carebot s.r.o.

Pulmonary Emphysema

Emphysema

Multiple Pulmonary Nodules

Subcutaneous Emphysema

Solitary Pulmonary Nodule

Respiratory Tract Neoplasms

Thoracic Neoplasms

A total of 1,073 chest X-rays were acquired within the specified period at the department. The data collection remained intact and unaffected throughout the testing phase, ensuring the integrity of the dataset. The collected sample accurately represents the prevalence of findings within the observed population. After excluding ineligible studies such as X-rays from patients under 18 years of age, lateral projection X-rays, and scans of insufficient quality, a total of 956 relevant CXRs were identified for further assessment.

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.

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

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).

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

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.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization	Link
Retrospective Study of Carebot AI CXR Performance in Preclinical Practice	NCT05594485	Artificial Inte... Lung Diseases Pneumothorax Pleural Effusio... Cardiomegaly Lung Cancer Pulmonary Edema Consolidation Pneumonia Atelectasis Hilar Calcifica... Fracture Rib	Carebot AI CXR	18 Years -	Carebot s.r.o.
Multi-Reader Retrospective Study Examining Carebot AI CXR 2.0.21-v2.01 Implementation in Everyday Radiology Clinical Practice	NCT05963945	Pneumothorax Pulmonary Nodul... Atelectasis Subcutaneous Em... Cardiomegaly Consolidation Pleural Effusio...	Carebot AI CXR	18 Years -	Carebot s.r.o.

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