Search Results (9)

Lung cancer remains one of the main causes of cancer-related death globally and a significant global health concern. There is an urgent need for safer and more effective therapeutic alternatives despite notable progress in therapy; issues such as drug resistance, side effects, metastasis, and recurrence still affect patient outcome and quality of life. The aim of this review is to examine recent developments in the application of herbal-drug-loaded nanoparticles as a new strategy for treating lung cancer. A thorough examination of different drug delivery systems based on nanoparticles is provided, highlighting their function in improving the solubility, bioavailability, and targeted delivery of herbal compounds. In addition, the review evaluates the biomarkers used for targeted therapy and examines how new personalised treatment approaches like wearable electronic patches, robotics-assisted interventions, smartphone-enabled therapies, AI-driven diagnostics, and lung-on-a-chip technologies can be integrated to improve the accuracy and effectiveness of lung cancer treatment. In conclusion, the combination of personalised medicine and nanotechnology may lead to revolutionary changes in lung cancer treatment in the future. Full article

Background: Artificial intelligence (AI) is rapidly transforming thoracic surgery by enhancing diagnostic accuracy, surgical precision, intraoperative guidance, and postoperative management. AI-driven technologies, including machine learning (ML), deep learning, computer vision, and robotic-assisted surgery, have the potential to optimize clinical workflows and improve patient outcomes. However, challenges such as data integration, ethical concerns, and regulatory barriers must be addressed to ensure AI’s safe and effective implementation. This review aims to analyze the current applications, benefits, limitations, and future directions of AI in thoracic surgery. Methods: This review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was performed using PubMed, Scopus, Web of Science, and Cochrane Library for studies published up to January 2025. Relevant articles were selected based on predefined inclusion and exclusion criteria, focusing on AI applications in thoracic surgery, including diagnostics, robotic-assisted surgery, intraoperative guidance, and postoperative care. A risk of bias assessment was conducted using the Cochrane Risk of Bias Tool and ROBINS-I for non-randomized studies. Results: Out of 279 identified studies, 36 met the inclusion criteria for qualitative synthesis, highlighting AI’s growing role in diagnostic accuracy, surgical precision, intraoperative guidance, and postoperative care in thoracic surgery. AI-driven imaging analysis and radiomics have improved pulmonary nodule detection, lung cancer classification, and lymph node metastasis prediction, while robotic-assisted thoracic surgery (RATS) has enhanced surgical accuracy, reduced operative times, and improved recovery rates. Intraoperatively, AI-powered image-guided navigation, augmented reality (AR), and real-time decision-support systems have optimized surgical planning and safety. Postoperatively, AI-driven predictive models and wearable monitoring devices have enabled early complication detection and improved patient follow-up. However, challenges remain, including algorithmic biases, a lack of multicenter validation, high implementation costs, and ethical concerns regarding data security and clinical accountability. Despite these limitations, AI has shown significant potential to enhance surgical outcomes, requiring further research and standardized validation for widespread adoption. Conclusions: AI is poised to revolutionize thoracic surgery by enhancing decision-making, improving patient outcomes, and optimizing surgical workflows. However, widespread adoption requires addressing key limitations through multicenter validation studies, standardized AI frameworks, and ethical AI governance. Future research should focus on digital twin technology, federated learning, and explainable AI (XAI) to improve AI interpretability, reliability, and accessibility. With continued advancements and responsible integration, AI will play a pivotal role in shaping the next generation of precision thoracic surgery. Full article

CT-guided transthoracic core-needle biopsy (CT-TTNB) is a minimally invasive procedure that plays a crucial role in diagnosing pulmonary nodules. With high diagnostic yield and low complication rates, CT-TTNB is favored over traditional surgical biopsies, providing accuracy in detecting both malignant and benign conditions. This literature review aims to present a comprehensive overview of CT-TTNB, focusing on its indications, procedural techniques, diagnostic yield, and safety considerations. Studies published between 2013 and 2024 were systematically reviewed from PubMed, Web of Science, Scopus, and Cochrane Library using the SANRA methodology. The results highlight that CT-TTNB has a diagnostic yield of 85–95% and sensitivity rates for detecting malignancies between 92 and 97%. Several factors, including nodule size, lesion depth, needle passes, and imaging techniques, influence diagnostic success. Complications such as pneumothorax and pulmonary hemorrhage were noted, with incidence rates varying from 12 to 45% for pneumothorax and 4 to 27% for hemorrhage. Preventative strategies and management algorithms are essential for minimizing and addressing these risks. In conclusion, CT-TTNB remains a reliable and effective method for diagnosing pulmonary nodules, particularly in peripheral lung lesions. Advancements such as PET/CT fusion imaging, AI-assisted biopsy planning, and robotic systems further enhance precision and safety. This review emphasizes the importance of careful patient selection and procedural planning to maximize outcomes while minimizing risks, ensuring that CT-TTNB continues to be an indispensable tool in pulmonary diagnostics. Full article

The diagnostic process in Intensive Care Units has been revolutionized by ultrasonography and accelerated by artificial intelligence. Patients in critical condition are often sonoanatomically challenging, with time constraints being an additional stress factor. In this paper, we describe the technology behind the development of AI systems to support diagnostic ultrasound in intensive care units. Among the AI-based solutions, the focus was placed on systems supporting cardiac ultrasound, such as Smart-VTI, Auto-VTI, SmartEcho Vue, AutoEF, Us2.ai, and Real Time EF. Solutions to assist hemodynamic assessment based on the evaluation of the inferior vena cava, such as Smart-IVC or Auto-IVC, as well as to facilitate ultrasound assessment of the lungs, such as Smart B-line or Auto B-line, and to help in the estimation of gastric contents, such as Auto Gastric Antrum, were also discussed. All these solutions provide doctors with support by making it easier to obtain appropriate diagnostically correct ultrasound images by automatically performing time-consuming measurements and enabling real-time analysis of the obtained data. Artificial intelligence will most likely be used in the future to create advanced systems facilitating the diagnostic and therapeutic process in intensive care units. Full article

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients. Full article

This study aimed to address three questions in AI-assisted COVID-19 diagnostic systems: (1) How does a CNN model trained on one dataset perform on test datasets from disparate medical centers? (2) What accuracy gains can be achieved by enriching the training dataset with new images? (3) How can learned features elucidate classification results, and how do they vary among different models? To achieve these aims, four CNN models—AlexNet, ResNet-50, MobileNet, and VGG-19—were trained in five rounds by incrementally adding new images to a baseline training set comprising 11,538 chest X-ray images. In each round, the models were tested on four datasets with decreasing levels of image similarity. Notably, all models showed performance drops when tested on datasets containing outlier images or sourced from other clinics. In Round 1, 95.2~99.2% accuracy was achieved for the Level 1 testing dataset (i.e., from the same clinic but set apart for testing only), and 94.7~98.3% for Level 2 (i.e., from an external clinic but similar). However, model performance drastically decreased for Level 3 (i.e., outlier images with rotation or deformation), with the mean sensitivity plummeting from 99% to 36%. For the Level 4 testing dataset (i.e., from another clinic), accuracy decreased from 97% to 86%, and sensitivity from 99% to 67%. In Rounds 2 and 3, adding 25% and 50% of the outlier images to the training dataset improved the average Level-3 accuracy by 15% and 23% (i.e., from 56% to 71% to 83%). In Rounds 4 and 5, adding 25% and 50% of the external images increased the average Level-4 accuracy from 81% to 92% and 95%, respectively. Among the models, ResNet-50 demonstrated the most robust performance across the five-round training/testing phases, while VGG-19 persistently underperformed. Heatmaps and intermediate activation features showed visual correlations to COVID-19 and pneumonia X-ray manifestations but were insufficient to explicitly explain the classification. However, heatmaps and activation features at different rounds shed light on the progression of the models’ learning behavior. Full article

The deep learning approach has recently attracted much attention for its outstanding performance to assist in clinical diagnostic tasks, notably in computer-aided solutions. Computer-aided solutions are being developed using chest radiography to identify lung diseases. A chest X-ray image is one of the most often utilized diagnostic imaging modalities in computer-aided solutions since it produces non-invasive standard-of-care data. However, the accurate identification of a specific illness in chest X-ray images still poses a challenge due to their high inter-class similarities and low intra-class variant abnormalities, especially given the complex nature of radiographs and the complex anatomy of the chest. In this paper, we proposed a deep-learning-based solution to classify four lung diseases (pneumonia, pneumothorax, tuberculosis, and lung cancer) and healthy lungs using chest X-ray images. In order to achieve a high performance, the EfficientNet B7 model with the pre-trained weights of ImageNet trained by Noisy Student was used as a backbone model, followed by our proposed fine-tuned layers and hyperparameters. Our study achieved an average test accuracy of 97.42%, sensitivity of 95.93%, and specificity of 99.05%. Additionally, our findings were utilized as diagnostic supporting software in OView-AI system (computer-aided application). We conducted 910 clinical trials and achieved an AUC confidence interval (95% CI) of the diagnostic results in the OView-AI system of 97.01%, sensitivity of 95.68%, and specificity of 99.34%. Full article

Acute lower respiratory infection is a leading cause of death in developing countries. Hence, progress has been made for early detection and treatment. There is still a need for improved diagnostic and therapeutic strategies, particularly in resource-limited settings. Chest X-ray and computed tomography (CT) have the potential to serve as effective screening tools for lower respiratory infections, but the use of artificial intelligence (AI) in these areas is limited. To address this gap, we present a computer-aided diagnostic system for chest X-ray and CT images of several common pulmonary diseases, including COVID-19, viral pneumonia, bacterial pneumonia, tuberculosis, lung opacity, and various types of carcinoma. The proposed system depends on super-resolution (SR) techniques to enhance image details. Deep learning (DL) techniques are used for both SR reconstruction and classification, with the InceptionResNetv2 model used as a feature extractor in conjunction with a multi-class support vector machine (MCSVM) classifier. In this paper, we compare the proposed model performance to those of other classification models, such as Resnet101 and Inceptionv3, and evaluate the effectiveness of using both softmax and MCSVM classifiers. The proposed system was tested on three publicly available datasets of CT and X-ray images and it achieved a classification accuracy of 98.028% using a combination of SR and InceptionResNetv2. Overall, our system has the potential to serve as a valuable screening tool for lower respiratory disorders and assist clinicians in interpreting chest X-ray and CT images. In resource-limited settings, it can also provide a valuable diagnostic support. Full article

(1) Background: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Reverse transcription polymerase chain reaction (RT-PCR) remains the current gold standard for detecting SARS-CoV-2 infections in nasopharyngeal swabs. In Romania, the first reported patient to have contracted COVID-19 was officially declared on 26 February 2020. (2) Methods: This study proposes a federated learning approach with pre-trained deep learning models for COVID-19 detection. Three clients were locally deployed with their own dataset. The goal of the clients was to collaborate in order to obtain a global model without sharing samples from the dataset. The algorithm we developed was connected to our internal picture archiving and communication system and, after running backwards, it encountered chest CT changes suggestive for COVID-19 in a patient investigated in our medical imaging department on the 28 January 2020. (4) Conclusions: Based on our results, we recommend using an automated AI-assisted software in order to detect COVID-19 based on the lung imaging changes as an adjuvant diagnostic method to the current gold standard (RT-PCR) in order to greatly enhance the management of these patients and also limit the spread of the disease, not only to the general population but also to healthcare professionals. Full article