Multi-Layer Perceptron Classifier with the Proposed Combined Feature Vector of 3D CNN Features and Lung Radiomics Features for COPD Stage Classification

Transforming pulmonary health care: the role of artificial intelligence in diagnosis and treatment

INTRODUCTION

Respiratory diseases like pneumonia, asthma, and COPD are major global health concerns, significantly impacting morbidity and mortality rates worldwide.

AREAS COVERED

A selective search on PubMed, Google Scholar, and ScienceDirect (up to 2024) focused on AI in diagnosing and treating respiratory conditions like asthma, pneumonia, and COPD. Studies were chosen for their relevance to prediction models, AI-driven diagnostics, and personalized treatments. This narrative review highlights technological advancements, clinical applications, and challenges in integrating AI into standard practice, with emphasis on predictive tools, deep learning for imaging, and patient outcomes.

EXPERT OPINION

Despite these advancements, significant challenges remain in fully integrating AI into pulmonary health care. The need for large, diverse datasets to train AI models is critical, and concerns around data privacy, algorithmic transparency, and potential biases must be carefully managed. Regulatory frameworks also need to evolve to address the unique challenges posed by AI in health care. However, with continued research and collaboration between technology developers, clinicians, and policymakers, AI has the potential to revolutionize pulmonary health care, ultimately leading to more effective, efficient, and personalized care for patients.

Voting-ac4C:Pre-trained large RNA language model enhances RNA N4-acetylcytidine site prediction

RNA N4-acetylcytidine (ac4C) modification plays a crucial role in gene expression regulation. However, existing prediction methods face limitations in capturing RNA sequence features, particularly in handling sequence complexity and long-range dependencies. To enhance the accuracy of RNA-ac4C modification sites prediction, this study introduces, for the first time, the transformer-based RNAErnie pre-trained model, which deeply extracts semantic information from RNA sequences. This model is combined with six traditional feature extraction methods (such as One-hot, ENAC, etc.) to form a multidimensional feature set. On this basis, we propose the Voting-ac4C model, which utilizes a deep neural network for feature selection. The selected features are then fed into a soft voting ensemble learning model, integrating the strengths of various machine learning algorithms to predict RNA-ac4C modification sites. Experimental results demonstrate that compared to the state-of-the-art methods, Voting-ac4C achieves significant improvements across multiple metrics, including AUC, SN, SP, ACC, and MCC. This study provides a novel approach for RNA modification sites prediction and highlights the potential applications of pre-trained models in biological sequence analysis.

Automatic cardiothoracic ratio calculation based on lung fields abstracted from chest X-ray images without heart segmentation

Introduction

The cardiothoracic ratio (CTR) based on postero-anterior chest X-rays (P-A CXR) images is one of the most commonly used cardiac measurement methods and an indicator for initially evaluating cardiac diseases. However, the hearts are not readily observable on P-A CXR images compared to the lung fields. Therefore, radiologists often manually determine the CTR's right and left heart border points of the adjacent left and right lung fields to the heart based on P-A CXR images. Meanwhile, manual CTR measurement based on the P-A CXR image requires experienced radiologists and is time-consuming and laborious.

Methods

Based on the above, this article proposes a novel, fully automatic CTR calculation method based on lung fields abstracted from the P-A CXR images using convolutional neural networks (CNNs), overcoming the limitations to heart segmentation and avoiding errors in heart segmentation. First, the lung field mask images are abstracted from the P-A CXR images based on the pre-trained CNNs. Second, a novel localization method of the heart's right and left border points is proposed based on the two-dimensional projection morphology of the lung field mask images using graphics.

Results

The results show that the mean distance errors at the x-axis direction of the CTR's four key points in the test sets T1 (21 × 512 × 512 static P-A CXR images) and T2 (13 × 512 × 512 dynamic P-A CXR images) based on various pre-trained CNNs are 4.1161 and 3.2116 pixels, respectively. In addition, the mean CTR errors on the test sets T1 and T2 based on four proposed models are 0.0208 and 0.0180, respectively.

Discussion

Our proposed model achieves the equivalent performance of CTR calculation as the previous CardioNet model, overcomes heart segmentation, and takes less time. Therefore, our proposed method is practical and feasible and may become an effective tool for initially evaluating cardiac diseases.

A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis

Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. The early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings poses challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we develop a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures the localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieves the highest performance with accuracy, sensitivity, specificity, precision, F1-score, and combined AUC of 0.93 ± 0.04, 0.94 ± 0.04, 0.93 ± 0.08, 0.95 ± 0.05, 0.94 ± 0.04, and 0.97, respectively, in a five-fold cross-validation study with pulmonary sarcoidosis (n = 126) and LCa (n = 93) cases. A detailed ablation study showing the impact of CNN + ViT compared to CNN or ViT alone, and CT + radiomics input, compared to CT or radiomics alone, is also presented in this work. Overall, the AI model developed in this work offers promising potential for triaging the pulmonary sarcoidosis patients for timely diagnosis and treatment from chest CT.

Hemi-diaphragm detection of chest X-ray images based on convolutional neural network and graphics

BACKGROUND

Chest X-rays (CXR) are widely used to facilitate the diagnosis and treatment of critically ill and emergency patients in clinical practice. Accurate hemi-diaphragm detection based on postero-anterior (P-A) CXR images is crucial for the diaphragm function assessment of critically ill and emergency patients to provide precision healthcare for these vulnerable populations.

OBJECTIVE

Therefore, an effective and accurate hemi-diaphragm detection method for P-A CXR images is urgently developed to assess these vulnerable populations' diaphragm function.

METHODS

Based on the above, this paper proposes an effective hemi-diaphragm detection method for P-A CXR images based on the convolutional neural network (CNN) and graphics. First, we develop a robust and standard CNN model of pathological lungs trained by human P-A CXR images of normal and abnormal cases with multiple lung diseases to extract lung fields from P-A CXR images. Second, we propose a novel localization method of the cardiophrenic angle based on the two-dimensional projection morphology of the left and right lungs by graphics for detecting the hemi-diaphragm.

RESULTS

The mean errors of the four key hemi-diaphragm points in the lung field mask images abstracted from static P-A CXR images based on five different segmentation models are 9.05, 7.19, 7.92, 7.27, and 6.73 pixels, respectively. Besides, the results also show that the mean errors of these four key hemi-diaphragm points in the lung field mask images abstracted from dynamic P-A CXR images based on these segmentation models are 5.50, 7.07, 4.43, 4.74, and 6.24 pixels,respectively.

CONCLUSION

Our proposed hemi-diaphragm detection method can effectively perform hemi-diaphragm detection and may become an effective tool to assess these vulnerable populations' diaphragm function for precision healthcare.

Prediction of the benign and malignant nature of masses in COPD background based on Habitat-based enhanced CT radiomics modeling: A preliminary study

BACKGROUND

It is difficult to differentiate between chronic obstructive pulmonary disease (COPD)-peripheral bronchogenic carcinoma (COPD-PBC) and inflammatory masses.

OBJECTIVE

This study aims to predict COPD-PBC based on clinical data and preoperative Habitat-based enhanced CT radiomics (HECT radiomics) modeling.

METHODS

A retrospective analysis was conducted on clinical imaging data of 232 cases of postoperative pathological confirmed PBC or inflammatory masses. The PBC group consisted of 82 cases, while the non-PBC group consisted of 150 cases. A training set and a testing set were established using a 7:3 ratio and a time cutoff point. In the training set, multiple models were established using clinical data and radiomics texture changes within different enhanced areas of the CT mass (HECT radiomics). The AUC values of each model were compared using Delong's test, and the clinical net benefit of the models was tested using decision curve analysis (DCA). The models were then externally validated in the testing set, and a nomogram of predicting COPD-PBC was created.

RESULTS

Univariate analysis confirmed that female gender, tumor morphology, CEA, Cyfra21-1, CT enhancement pattern, and Habitat-Radscore B/C were predictive factors for COPD-PBC (P< 0.05). The combination model based on these factors had significantly higher predictive performance [AUC: 0.894, 95% CI (0.836-0.936)] than the clinical data model [AUC: 0.758, 95% CI (0.685-0.822)] and radiomics model [AUC: 0.828, 95% CI (0.761-0.882)]. DCA also confirmed the higher clinical net benefit of the combination model, which was validated in the testing set. The nomogram developed based on the combination model helped predict COPD-PBC.

CONCLUSION

The combination model based on clinical data and Habitat-based enhanced CT radiomics can help differentiate COPD-PBC, providing a new non-invasive and efficient method for its diagnosis, treatment, and clinical decision-making.