Chest X-rays (CXR) are routinely used to diagnose lung and heart conditions. AI based Bone suppression imaging (BSI) aims to enhance accuracy in identifying chest anomalies by eliminating bony structures such as the ribs, clavicles, and scapula from CXRs. The aim of this retrospective study was to assess the clinical value of BSI in detecting pneumonia.
Ninety-nine emergency patients with suspected pneumonia underwent erect postero-anterior CXRs. The BSI processing system was used to generate corresponding bone-suppressed images for the 99 radiographs. Each patient had undergone a computed tomography (CT) examination within 48 h, considered the standard of reference. Two blinded readers separately analyzed images, indicating confidence levels regarding signs of pneumonia for each lung separated in three fields, first with standard images, then with BSI. Sensitivity, specificity, predictive values, and readers' certitude were calculated, and inter-reader agreement was evaluated with the kappa statistic.
Out of the 99 included cases, 39 cases of pneumonia were diagnosed (39.4%). Of the remaining 60 patients, 14 presented only pleural effusions (14.1%). BSI images led to a significant increase in false positives (+251%) and significantly affected one reader's diagnosis and certitude, decreasing accuracy (up to 17%) and specificity (up to 14%). Sensitivity increased by 66% with BSI. Inter-reader agreement ranged from weak to moderate (0.113-0.53) and did not improve with BSI. For both readers, BSI images were read with significantly lesser certitude than standard images.
BSI did not add clinical value in pneumonia detection on CXR due to a significant increase in false positive results and a decrease one readers' certitude.
The study emphasizes the importance of proper clinical training before implementing new post-processing and artificial intelligence (AI) tools in clinical practice.