Enhancing Radiological Reporting in Head and Neck Cancer: Converting Free-Text CT Scan Reports to Structured Reports Using Large Language Models.

 The aim of this study was to assess efficacy of large language models (LLMs) for converting free-text computed tomography (CT) scan reports of head and neck cancer (HNCa) patients into a structured format using a predefined template.  A retrospective study was conducted using 150 CT reports of HNCa patients. A comprehensive structured reporting template for HNCa CT scans was developed, and the Generative Pre-trained Transformer 4 (GPT-4) was initially used to convert 50 CT reports into a structured format using this template.

Comparative Evaluation of Large Language Models for Translating Radiology Reports into Hindi.

 The aim of this study was to compare the performance of four publicly available large language models (LLMs)-GPT-4o, GPT-4, Gemini, and Claude Opus-in translating radiology reports into simple Hindi.  In this retrospective study, 100 computed tomography (CT) scan report impressions were gathered from a tertiary care cancer center. Reference translations of these impressions into simple Hindi were done by a bilingual radiology staff in consultation with a radiologist.

Simulation training in mammography with AI-generated images: a multireader study.

Objectives: The interpretation of mammograms requires many years of training and experience. Currently, training in mammography, like the rest of diagnostic radiology, is through institutional libraries, books, and experience accumulated over time. We explore whether artificial Intelligence (AI)-generated images can help in simulation education and result in measurable improvement in performance of residents in training.

Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature.

 Although abundant literature is currently available on the use of deep learning for breast cancer detection in mammography, the quality of such literature is widely variable.  To evaluate published literature on breast cancer detection in mammography for reproducibility and to ascertain best practices for model design.  The PubMed and Scopus databases were searched to identify records that described the use of deep learning to detect lesions or classify images into cancer or noncancer.

Data Privacy in Healthcare: In the Era of Artificial Intelligence.

Data Privacy has increasingly become a matter of concern in the era of large public digital respositories of data. This is particularly true in healthcare where data can be misused if traced back to patients, and brings with itself a myriad of possibilities. Bring custodians of data, as well as being at the helm of disigning studies and products that can potentially benefit products, healthcare professionals often find themselves unsure about ethical and legal constraints that undelie data sharing.

A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models.

The presence of lung metastases in patients with primary malignancies is an important criterion for treatment management and prognostication. Computed tomography (CT) of the chest is the preferred method to detect lung metastasis. However, CT has limited efficacy in differentiating metastatic nodules from benign nodules (e.

Fully automatic deep learning-based lung parenchyma segmentation and boundary correction in thoracic CT scans.

Purpose: The proposed work aims to develop an algorithm to precisely segment the lung parenchyma in thoracic CT scans. To achieve this goal, the proposed technique utilized a combination of deep learning and traditional image processing algorithms. The initial step utilized a trained convolutional neural network (CNN) to generate preliminary lung masks, followed by the proposed post-processing algorithm for lung boundary correction.

Cervical Erector Spinae Plane Block for Giant Dermatofibrosarcoma Protuberans of Neck and Chest Wall: A Case Report.

We encountered a giant dermatofibrosarcoma protuberans (DFSP) of the neck and chest wall which presented a challenge in terms of perioperative analgesia management. In recent years, erector spinae plane (ESP) block has emerged as an effective and safe analgesia technique for various surgical procedures as well as for chronic neuropathic pain without any untoward complications. A continuous lower cervical ESP block can be used successfully as an effective analgesic technique for extensive DFSP surgery involving the neck and chest wall area.

Novel Coaxial Technique of Percutaneous Nephrostomy in Young Children: A Feasibility Study.

Aims: The conventional Seldinger and trocar techniques of percutaneous nephrostomy (PCN) have inherent limitations in infants and younger children. We studied the role of a novel coaxial technique of PCN in children under the age of 5 years in comparison to the conventional techniques.

Materials And Methods: This was a single-center feasibility trial based on 24 consecutive patients ( = 24 kidneys) under the age of 5 years, conducted over 12 months, substratified into Group I ( = 10): PCN with conventional Seldinger ( = 2) and trocar ( = 8) techniques and Group II ( = 14): PCN with proposed coaxial technique.

Platform-Based Internationalization of Smaller Firms: The Role of Government Policy.

Unlabelled: Governments are recognizing anticompetitive concerns and market distortions associated with the rise of e-commerce platforms. Thus, policies are being crafted to level the playing field between large platform operators and small platform sellers. In addition, policies mitigating barriers to internationalization associated with using e-commerce platforms are also being developed.

Robotic ultrasound: An initial feasibility study.

Background: Performing ultrasound during the current pandemic time is quite challenging. To reduce the chances of cross-infection and keep healthcare workers safe, a robotic ultrasound system was developed, which can be controlled remotely. It will also pave way for broadening the reach of ultrasound in remote distant rural areas as well.

Risk Factors of Severity and Mortality Among COVID-19 Patients: A Prospective Observational Study From a Tertiary Care Center.

Introduction The COVID-19 pandemic has been a major public health threat for the past three years. The RNA virus has been constantly evolving, changing the manifestations and progression of the disease. Some factors which impact the progression to severe COVID-19 or mortality include comorbidities such as diabetes mellitus, hypertension, and obesity.

Ultra-high resolution, multi-scale, context-aware approach for detection of small cancers on mammography.

While detection of malignancies on mammography has received a boost with the use of Convolutional Neural Networks (CNN), detection of cancers of very small size remains challenging. This is however clinically significant as the purpose of mammography is early detection of cancer, making it imperative to pick them up when they are still very small. Mammography has the highest spatial resolution (image sizes as high as 3328 × 4096 pixels) out of all imaging modalities, a requirement that stems from the need to detect fine features of the smallest cancers on screening.

Basic principles of AI simplified for a Medical Practitioner: Pearls and Pitfalls in Evaluating AI algorithms.

With the rapid integration of artificial intelligence into medical practice, there has been an exponential increase in the number of scientific papers and industry players offering models designed for various tasks. Understanding these, however, is difficult for a radiologist in practice, given the core mathematical principles and complicated terminology involved. This review aims to elucidate the core mathematical concepts of both machine learning and deep learning models, explaining the various steps and common terminology in common layman language.

Breast cancer liver metastases: systematic review and time to event meta-analysis with comparison between available treatments.

Introduction: The current gold standard treatment for breast cancer liver metastases (BCLM) is systemic chemotherapy and/or hormonal therapy. Nonetheless, greater consideration has been given to local therapeutic strategies in recent years. We sought to compare survival outcomes for available systemic and local treatments for BCLM, specifically surgical resection and radiofrequency ablation.