In the study, we first introduce a novel AI-based system (MOM-ClaSeg) for multiple abnormality/disease detection and diagnostic report generation on PA/AP CXR images, which was recently developed by applying augmented Mask RCNN deep learning and Decision Fusion Networks. We then evaluate performance of MOM-ClaSeg system in assisting radiologists in image interpretation and diagnostic report generation through a multi-reader-multi-case (MRMC) study. A total of 33,439 PA/AP CXR images were retrospectively collected from 15 hospitals, which were divided into an experimental group of 25,840 images and a control group of 7,599 images with and without processed by MOM-ClaSeg system, respectively. In this MRMC study, 6 junior radiologists (5~10yr experience) first read these images and generated initial diagnostic reports with/without viewing MOM-ClaSeg-generated results. Next, the initial reports were reviewed by 2 senior radiologists (>15yr experience) to generate final reports. Additionally, 3 consensus expert radiologists (>25yr experience) reconciled the potential difference between initial and final reports. Comparison results showed that usingMOM-ClaSeg, diagnostic sensitivity of junior radiologists increased significantly by 18.67% (from 70.76% to 89.43%, P<0.001), while specificity decreased by 3.36% (from 99.49% to 96.13%, P<0.001). Average reading/diagnostic time in experimental group with MOM-ClaSeg reduced by 27.07% (P<0.001), with a particularly significant reduction of 66.48% (P<0.001) on abnormal images, indicating that MOM-ClaSeg system has potential for fast lung abnormality/disease triaging. This study demonstrates feasibility of applying the first AI-based system to assist radiologists in image interpretation and diagnostic report generation, which is a promising step toward improved diagnostic performance and productivity in future clinical practice.
It’s widely known that HIV infection would cause white matter integrity impairments. Nevertheless, it is still unclear that how the white matter anatomical structural connections are affected by HIV infection. In the current study, we employed a multivariate pattern analysis to explore the HIV-related white matter connections alterations. Forty antiretroviraltherapy- naïve HIV patients and thirty healthy controls were enrolled. Firstly, an Automatic Anatomical Label (AAL) atlas based white matter structural network, a 90 × 90 FA-weighted matrix, was constructed for each subject. Then, the white matter connections deprived from the structural network were entered into a lasso-logistic regression model to perform HIV-control group classification. Using leave one out cross validation, a classification accuracy (ACC) of 90% (P=0.002) and areas under the receiver operating characteristic curve (AUC) of 0.96 was obtained by the classification model. This result indicated that the white matter anatomical structural connections contributed greatly to HIV-control group classification, providing solid evidence that the white matter connections were affected by HIV infection. Specially, 11 white matter connections were selected in the classification model, mainly crossing the regions of frontal lobe, Cingulum, Hippocampus, and Thalamus, which were reported to be damaged in previous HIV studies. This might suggest that the white matter connections adjacent to the HIV-related impaired regions were prone to be damaged.
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