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Can commercial AI-based CAD software improve the detection rate
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Can commercial AI-based CAD software improve the detection rate
In a recent study published in the Radiology, researchers performed a single-center, open-label randomized controlled trial (RCT) to investigate whether commercial artificial intelligence (AI)-debased computer-aided design (CAD) system could improve the detection rate of actionable lung nodules on chest radiographs.To get more news about cad computer aided design, you can visit shine news official website.
Actionable nodules are Lung Imaging Reporting and Data System (Lung-RADS), Category 4 nodules. These might be solid nodules larger than eight mm or subsolid nodules with a solid area spanning over six mm.
Studies have not prospectively explored the impact of AI–based CAD software in real-world settings.About the study
In the present study, researchers recruited individuals who underwent chest radiography between June 2020 and December 2021 at a health clinic of Seoul National University Hospital. They used a self-reported health questionnaire to gather baseline characteristics of all the participants, including age, gender, comorbidities (e.g., diabetes), history of lung cancer, surgery, tuberculosis, family history of lung cancer, and other malignancies.First, the researchers acquired all chest radiographs in the posteroanterior projection with no lateral views. The image was subsequently allocated a unique five-digit random number by the AI-picture archiving and communication system (PACS) server. Next, the PACS server randomized the participants in a 1:1 ratio into either the AI or non-AI group.
A highly experienced radiologist interpreted the radiograph of each participant aided by AI-based CAD in the AI group. For those in the non-AI group, the radiologists interpreted chest radiographs without AI-based CAD results. The radiologists used an AI-PACS-implemented structured reporting system to interpret chest radiographs from both groups.
For participants in the AI group, the AI-PACS server overlaid the results of AI-based CAD onto the chest radiograph and sent them back to the conventional PACS server. On the contrary, it blinded the AI-based CAD results in the non-AI group. Later, clinicians at the health center viewed these images and radiologic reports for clinical practice. This system also analyzed frontal chest radiographs to assess thoracic abnormalities, i.e., pneumonia, pneumothorax, and a pulmonary nodule, on a percentage scale of zero to 100% and depicted them as heat maps.
The primary outcome was the detection rate of actionable lung nodules confirmed on CT scans obtained within three months after chest radiography. The detection rate was the number of true-positive chest radiographs divided by the cumulative chest radiographs. Additionally, the team assessed a few secondary outcomes, such as the false-referral and positive-report rates. The team used univariable logistic regression analyses to compare clinical outcomes between the AI and non-AI groups.