Development and validation of a deep learning model for multicategory pneumonia classification on chest computed tomography: a multicenter and multireader study.

Background: Accurate diagnosis of pneumonia is vital for effective disease management and mortality reduction, but it can be easily confused with other conditions on chest computed tomography (CT) due to an overlap in imaging features. We aimed to develop and validate a deep learning (DL) model based on chest CT for accurate classification of viral pneumonia (VP), bacterial pneumonia (BP), fungal pneumonia (FP), pulmonary tuberculosis (PTB), and no pneumonia (NP) conditions. Methods: In total, 1,776 cases from five hospitals in different regions were retrospectively collected from September 2019 to June 2023. All cases were enrolled according to inclusion and exclusion criteria, and ultimately 1,611 cases were used to develop the DL model with 5-fold cross-validation, with 165 cases being used as the external test set. Five radiologists blindly reviewed the images from the internal and external test sets first without and then with DL model assistance. Precision, recall, F1-score, weighted F1-average, and area under the curve (AUC) were used to evaluate the model performance. Results: The F1-scores of the DL model on the internal and external test sets were, respectively, 0.947 [95% confidence interval (CI): 0.936-0.958] and 0.933 (95% CI: 0.916-0.950) for VP, 0.511 (95% CI: 0.487-0.536) and 0.591 (95% CI: 0.557-0.624) for BP, 0.842 (95% CI: 0.824-0.860) and 0.848 (95% CI: 0.824-0.873) for FP, 0.843 (95% CI: 0.826-0.861) and 0.795 (95% CI: 0.767-0.822) for PTB, and 0.975 (95% CI: 0.968-0.983) and 0.976 (95% CI: 0.965-0.986) for NP, with a weighted F1-average of 0.883 (95% CI: 0.867-0.898) and 0.846 (95% CI: 0.822-0.871), respectively. The model performed well and showed comparable performance in both the internal and external test sets. The F1-score of the DL model was higher than that of radiologists, and with DL model assistance, radiologists achieved a higher F1-score. On the external test set, the F1-score of the DL model (F1-score 0.848; 95% CI: 0.824-0.873) was higher than that of the radiologists (F1-score 0.541; 95% CI: 0.507-0.575) as was its precision for the other three pneumonia conditions (all P values <0.001). With DL model assistance, the F1-score for FP (F1-score 0.541; 95% CI: 0.507-0.575) was higher than that achieved without assistance (F1-score 0.778; 95% CI: 0.750-0.807) as was its precision for the other three pneumonia conditions (all P values <0.001). Conclusions: The DL approach can effectively classify pneumonia and can help improve radiologists' performance, supporting the full integration of DL results into the routine workflow of clinicians.

Regional homogeneity alterations in multifrequency bands in patients with extracranial multi-organ tuberculosis: a prospective cross-sectional study.

Background: This study aimed to clarify the spontaneous neural activity in the conventional frequency band (0.01-0.08 Hz) and 2 subfrequency bands (slow-4: 0.027-0.073 Hz; slow-5: 0.01-0.027 Hz) in patients with extracranial multi-organ tuberculosis (EMTB) through regional homogeneity (ReHo) analysis. Methods: In all, 32 patients with EMTB and 31 healthy controls (HCs) were assessed by resting-state functional magnetic resonance imaging (rs-fMRI) scans to clarify the abnormal spontaneous neural activity through ReHo analysis in the conventional frequency band and 2 subfrequency bands. Results: Compared with the HCs, the patients with EMTB exhibited decreased ReHo in the left postcentral gyrus [t=-4.79; 95% confidence interval (CI): -0.79 to -0.31] and the left superior cerebellum (t=-4.45; 95% CI: -0.54 to -0.21) in the conventional band. Conversely, increased ReHo was observed in the right middle occipital gyrus (t=3.94; 95% CI: 0.18-0.53). In the slow-4 band, patients with EMTB only exhibited decreased ReHo in the superior cerebellum (t=-4.69; 95% CI: -0.54 to -0.22); meanwhile, in the slow-5 band, these patients exhibited decreased ReHo in the right postcentral gyrus (t=-3.76; 95% CI: -0.74 to -0.21) and the left superior cerebellum (t=-5.20, 95% CI: -0.72 to -0.31). After Bonferroni correction, no significant correlation was observed between the ReHo values in clusters showing significant between-group differences and cognitive test scores. Conclusions: ReHo showed abnormal synchronous neural activity in patients with EMTB in different frequency bands, which provides a novel understanding of the pathological mechanism of EMTB.

New and Efficient Electrocatalyst for Hydrogen Production from Water Splitting: Inexpensive, Robust Metallic Glassy Ribbons Based on Iron and Cobalt.

Efficient, stable electrocatalysts are required to promote the hydrogen evolution reaction (HER) to obtain hydrogen as a clean, sustainable fuel via water splitting. In the present work, ribbons of the metallic glass Fe40Co40P13C7 were produced using a conventional melt-spinning technique and assessed as electrocatalysts for HER. In 0.5 M H2SO4, these ribbons generated an overpotential of 118 mV at a current density of 10 mA cm-2. This overpotential remained essentially constant over 20 h under these conditions. On the basis of the excellent properties, these glassy ribbons represent a new type of highly active, robust HER catalyst suitable for practical applications.