ChestSuite is a software bundle that aggregates the functionalities and outputs of three FDA clearances: Aorta-CAD (AC) and Lung-CAD (LC), and Lung-CAD (LC). The indications for use are as follows: AC is a computer-assisted detection (CADe) software device that analyzes chest radiograph studies for suspicious regions of interest (ROIs). The device uses a deep learning algorithm to identify ROIs and produces boxes around the ROIs. The boxes are labeled with one of the following radiographic findings: Aortic calcification or Dilated aorta. AC is intended for use as a concurrent reading aid for physicians looking for ROIs with radiographic findings suggestive of Aortic Atherosclerosis or Aortic Ectasia. It does not replace the role of the physician or of other diagnostic testing in the standard of care. AC is indicated for adults only. LC is a computer-assisted detection (CADe) software device that analyzes chest radiograph studies for lung hyperinflation. The device uses a deep learning algorithm to identify ROIs with lung hyperinflation and produces boxes around the ROIs. LC is intended for use as a concurrent reading aid for physicians interpreting chest X-rays. The device is not intended for clinical diagnosis of any disease. It does not replace the role of the physician or of other diagnostic testing in the standard of care for lung parenchymal findings. LC is indicated for adults only.LC is a computer-assisted detection (CADe) software device that analyzes chest radiograph studies for interstitial thickening. The device uses a deep learning algorithm to identify ROIs with interstitial thickening and produces boxes around the ROIs. LC is intended for use as a concurrent reading aid for physicians interpreting chest X-rays. The device is not intended for clinical diagnosis of any disease. It does not replace the role of the physician or of other diagnostic testing in the standard of care for lung parenchymal findings. Lung-CAD is indicated for adults only.
An interpretive software program intended to be used to analyse non-dental x-ray images (e.g., chest, mammograms, tomograms) to detect and localize suspected abnormalities (e.g., tumours, emphysema, tuberculosis, blunted costophrenic angle) and possibly provide results as clinically relevant tags. It typically utilizes artificial intelligence (AI) and deep learning techniques, and may be compatible with radiology information systems, data formats, and medical imaging software programs [e.g., picture archiving and communication system (PACS), digital imaging and communications in medicine (DICOM) format].