Lung Ultrasound Reduces Chest X-rays in Postoperative Care after Thoracic Surgery: Is There a Role for Artificial Intelligence?-Systematic Review

The role of artificial intelligence in the diagnosis, imaging, and treatment of thoracic empyema

PURPOSE OF REVIEW

The management of thoracic empyema is often complicated by diagnostic delays, recurrence, treatment failures and infections with antibiotic resistant bacteria. The emergence of artificial intelligence (AI) in healthcare, particularly in clinical decision support, imaging, and diagnostic microbiology raises great expectations in addressing these challenges.

RECENT FINDINGS

Machine learning (ML) and AI models have been applied to CT scans and chest X-rays to identify and classify pleural effusions and empyema with greater accuracy. AI-based analyses can identify complex imaging features that are often missed by the human eye, improving diagnostic precision. AI-driven decision-support algorithms could reduce time to diagnosis, improve antibiotic stewardship, and enhance more precise and less invasive surgical therapy, significantly improving clinical outcomes and reducing inpatient hospital stays.

SUMMARY

ML and AI can analyse large datasets and recognize complex patterns and thus have the potential to enhance diagnostic accuracy, preop planning for thoracic surgery, and optimize surgical treatment strategies, antibiotic therapy, antibiotic stewardship, monitoring complications, and long-term patient management outcomes.

Minimally Invasive Sampling of Mediastinal Lesions

This narrative review examines the existing literature on minimally invasive image-guided sampling techniques of mediastinal lesions gathered from international databases (Medline, PubMed, Scopus, and Google Scholar). Original studies, systematic reviews with meta-analyses, randomized controlled trials, and case reports published between January 2009 and November 2023 were included. Four authors independently conducted the search to minimize bias, removed duplicates, and selected and evaluated the studies. The review focuses on the recent advancements in mediastinal sampling techniques, including EBUS-TBNA, EUS-FNA and FNB, IFB, and nodal cryobiopsy. The review highlights the advantages of an integrated approach using these techniques for diagnosing and staging mediastinal diseases, which, when used competently, significantly increase diagnostic yield and accuracy.

AI-Driven Thoracic X-ray Diagnostics: Transformative Transfer Learning for Clinical Validation in Pulmonary Radiography

Our research evaluates advanced artificial (AI) methodologies to enhance diagnostic accuracy in pulmonary radiography. Utilizing DenseNet121 and ResNet50, we analyzed 108,948 chest X-ray images from 32,717 patients and DenseNet121 achieved an area under the curve (AUC) of 94% in identifying the conditions of pneumothorax and oedema. The model's performance surpassed that of expert radiologists, though further improvements are necessary for diagnosing complex conditions such as emphysema, effusion, and hernia. Clinical validation integrating Latent Dirichlet Allocation (LDA) and Named Entity Recognition (NER) demonstrated the potential of natural language processing (NLP) in clinical workflows. The NER system achieved a precision of 92% and a recall of 88%. Sentiment analysis using DistilBERT provided a nuanced understanding of clinical notes, which is essential for refining diagnostic decisions. XGBoost and SHapley Additive exPlanations (SHAP) enhanced feature extraction and model interpretability. Local Interpretable Model-agnostic Explanations (LIME) and occlusion sensitivity analysis further enriched transparency, enabling healthcare providers to trust AI predictions. These AI techniques reduced processing times by 60% and annotation errors by 75%, setting a new benchmark for efficiency in thoracic diagnostics. The research explored the transformative potential of AI in medical imaging, advancing traditional diagnostics and accelerating medical evaluations in clinical settings.