Diagnosis of Active Pulmonary Tuberculosis and Community Acquired Pneumonia Using Convolution Neural Network Based on Transfer Learning

Space-CNN: a decision classification method based on EEG signals from different brain regions

Decision-making plays a critical role in an individual's interpersonal interactions and cognitive processes. Due to the issue of strong subjectivity in the classification research of art design decisions, we utilize the relatively objective electroencephalogram (EEG) to explore design decision problems. However, different regions of the brain do not have the same influence on the design decision classification, so this paper proposes a spatial feature based convolutional neural network (space-CNN) to explore the problem of decision classification of EEG signals from different regions. We recruit 16 subjects to collect their EEG data while viewing four stimulation patterns. After noise reduction of the raw data by discrete wavelet transform (DWT), the EEG image is generated by combining it with the spatial features of the EEG signal, which is used as the input of CNN. Our experimental results show that the degree of influence of different brain regions on decision-making is parietal lobe > frontal lobe > occipital lobe > temporal lobe. In addition, the average accuracy of space-CNN reaches 86.13%, which is about 6% higher than similar studies.

Ten Issues for Updating in Community-Acquired Pneumonia: An Expert Review

Community-acquired pneumonia represents the third-highest cause of mortality in industrialized countries and the first due to infection. Although guidelines for the approach to this infection model are widely implemented in international health schemes, information continually emerges that generates controversy or requires updating its management. This paper reviews the most important issues in the approach to this process, such as an aetiologic update using new molecular platforms or imaging techniques, including the diagnostic stewardship in different clinical settings. It also reviews both the Intensive Care Unit admission criteria and those of clinical stability to discharge. An update in antibiotic, in oxygen, or steroidal therapy is presented. It also analyzes the management out-of-hospital in CAP requiring hospitalization, the main factors for readmission, and an approach to therapeutic failure or rescue. Finally, the main strategies for prevention and vaccination in both immunocompetent and immunocompromised hosts are reviewed.

Development and validation of a 3D-convolutional neural network model based on chest CT for differentiating active pulmonary tuberculosis from community-acquired pneumonia

PURPOSE

To develop and validate a 3D-convolutional neural network (3D-CNN) model based on chest CT for differentiating active pulmonary tuberculosis (APTB) from community-acquired pneumonia (CAP).

MATERIALS AND METHODS

Chest CT images of APTB and CAP patients diagnosed in two imaging centers (n = 432 in center A and n = 61 in center B) were collected retrospectively. The data in center A were divided into training, validation and internal test sets, and the data in center B were used as an external test set. A 3D-CNN was built using Keras deep learning framework. After the training, the 3D-CNN selected the model with the highest accuracy in the validation set as the optimal model, which was applied to the two test sets in centers A and B. In addition, the two test sets were independently diagnosed by two radiologists. The 3D-CNN optimal model was compared with the discrimination, calibration and net benefit of the two radiologists in differentiating APTB from CAP using chest CT images.

RESULTS

The accuracy of the 3D-CNN optimal model was 0.989 and 0.934 with the internal and external test set, respectively. The area-under-the-curve values with the 3D-CNN model in the two test sets were statistically higher than that of the two radiologists (all P < 0.05), and there was a high calibration degree. The decision curve analysis showed that the 3D-CNN optimal model had significantly higher net benefit for patients than the two radiologists.

CONCLUSIONS

3D-CNN has high classification performance in differentiating APTB from CAP using chest CT images. The application of 3D-CNN provides a new automatic and rapid diagnosis method for identifying patients with APTB from CAP using chest CT images.

Diagnostic Accuracy of the Artificial Intelligence Methods in Medical Imaging for Pulmonary Tuberculosis: A Systematic Review and Meta-Analysis

Tuberculosis (TB) remains one of the leading causes of death among infectious diseases worldwide. Early screening and diagnosis of pulmonary tuberculosis (PTB) is crucial in TB control, and tend to benefit from artificial intelligence. Here, we aimed to evaluate the diagnostic efficacy of a variety of artificial intelligence methods in medical imaging for PTB. We searched MEDLINE and Embase with the OVID platform to identify trials published update to November 2022 that evaluated the effectiveness of artificial-intelligence-based software in medical imaging of patients with PTB. After data extraction, the quality of studies was assessed using quality assessment of diagnostic accuracy studies 2 (QUADAS-2). Pooled sensitivity and specificity were estimated using a bivariate random-effects model. In total, 3987 references were initially identified and 61 studies were finally included, covering a wide range of 124,959 individuals. The pooled sensitivity and the specificity were 91% (95% confidence interval (CI), 89-93%) and 65% (54-75%), respectively, in clinical trials, and 94% (89-96%) and 95% (91-97%), respectively, in model-development studies. These findings have demonstrated that artificial-intelligence-based software could serve as an accurate tool to diagnose PTB in medical imaging. However, standardized reporting guidance regarding AI-specific trials and multicenter clinical trials is urgently needed to truly transform this cutting-edge technology into clinical practice.