DICOM LUT is a Key Step in Medical Image Preprocessing Towards AI Generalizability.

Image pre-processing has significant impact on performance of deep learning models in medicine; yet, there is no standardized method for DICOM pre-processing. In this study, we investigate the impact of two commonly used image preprocessing techniques, histogram equalization (HE) and values-of-interest look-up-table (VOI-LUT) transformations on the performance deep learning classifiers for chest X-rays (CXR). We generated two baseline datasets (raw pixel and standard DICOM processed) from our internal CXR dataset and then enhanced both with HE to create four distinct datasets.

Treatment of oropharyngeal cancer during the COVID-19 lockdown - outcomes for patients treated during the pandemic.

Background: The onset of the coronavirus disease 2019 (COVID-19) outbreak caused major interruptions to the entire healthcare network affecting referral, diagnosis and treatment pathways with the potential to affect cancer treatment outcomes. In Ireland a national lockdown was initiated in March 2020 involving a stay-at-home order with a limitation on travel, social interactions and closure of schools, universities and childcare facilities. We designed a retrospective study comparing treatment outcomes for patients with oropharyngeal cancer treated before and during the COVID pandemic.

Ultrahypofractionated Adjuvant Breast Radiation Therapy (± Boost) and Virtual Consultations: Patient Perspectives at 1 Year.

Purpose: In March 2020, a 1-week ultrahypofractionated adjuvant breast radiation therapy schedule, 26 Gy in 5 fractions, and telehealth were adopted to reduce the risk of COVID-19 for staff and patients. This study describes real-world 1-year late toxicity for ultrahypofractionation (including a sequential boost) and patient perspectives on this new schedule and telehealth workflows.

Methods And Materials: Consecutive patients were enrolled between March and August 2020.

RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models.

Deep learning techniques hold immense promise for advancing medical image analysis, particularly in tasks like image segmentation, where precise annotation of regions or volumes of interest within medical images is crucial but manually laborious and prone to interobserver and intraobserver biases. As such, deep learning approaches could provide automated solutions for such applications. However, the potential of these techniques is often undermined by challenges in reproducibility and generalizability, which are key barriers to their clinical adoption.

Identifying and prioritizing inefficiency causes in Iran's health system.

Background: Enhancing efficiency is crucial in addressing the escalating scarcity of healthcare resources. It plays a pivotal role in achieving Universal Health Coverage (UHC), with the ultimate goal of ensuring health equity for all. A fundamental strategy to bolster efficiency involves pinpointing the underlying causes of inefficiency within the healthcare system through empirical research.

Comprehensive segmentation of gray matter structures on T1-weighted brain MRI: A Comparative Study of CNN, CNN hybrid-transformer or -Mamba architectures.

Background And Purpose: Recent advances in deep learning have shown promising results in medical image analysis and segmentation. However, most brain MRI segmentation models are limited by the size of their datasets and/or the number of structures they can identify. This study evaluates the performance of six advanced deep learning models in segmenting 122 brain structures from T1-weighted MRI scans, aiming to identify the most effective model for clinical and research applications.

Analyzing Racial Differences in Imaging Joint Replacement Registries Using Generative Artificial Intelligence: Advancing Orthopaedic Data Equity.

Background: Discrepancies in medical data sets can perpetuate bias, especially when training deep learning models, potentially leading to biased outcomes in clinical applications. Understanding these biases is crucial for the development of equitable healthcare technologies. This study employs generative deep learning technology to explore and understand radiographic differences based on race among patients undergoing total hip arthroplasty.

Accessible Double Nanohole Raman Tweezer Analysis of Single Nanoparticles.

Raman spectroscopy allows for material characterization of nanoparticles; however, probing individual nanoparticles requires an efficient way of isolating and enhancing the signal. Past works have used optical trapping with nanoapertures in metal films to measure the Raman spectra of individual nanoparticles; however, those works required custom laser tweezer systems that provided a transmission signal to verify trapping events as well as costly top-down nanofabrication. Here, we trapped Titania nanoparticles in a commercial Raman system using double nanoholes (DNH) and measured their spectra while trapped.

Development of a deep learning model for the automated detection of green pixels indicative of gout on dual energy CT scan.

Background: Dual-energy CT (DECT) is a non-invasive way to determine the presence of monosodium urate (MSU) crystals in the workup of gout. Color-coding distinguishes MSU from calcium following material decomposition and post-processing. Most software labels MSU as green and calcium as blue.

Overview of Artificial Intelligence Research Within Hip and Knee Arthroplasty.

Hip and knee arthroplasty are high-volume procedures undergoing rapid growth. The large volume of procedures generates a vast amount of data available for next-generation analytics. Techniques in the field of artificial intelligence (AI) can assist in large-scale pattern recognition and lead to clinical insights.

A Multi-View Deep Learning Model for Thyroid Nodules Detection and Characterization in Ultrasound Imaging.

Thyroid Ultrasound (US) is the primary method to evaluate thyroid nodules. Deep learning (DL) has been playing a significant role in evaluating thyroid cancer. We propose a DL-based pipeline to detect and classify thyroid nodules into benign or malignant groups relying on two views of US imaging.

Deep learning classification of pediatric spinal radiographs for use in large scale imaging registries.

Purpose: The purpose of this study is to develop and apply an algorithm that automatically classifies spine radiographs of pediatric scoliosis patients.

Methods: Anterior-posterior (AP) and lateral spine radiographs were extracted from the institutional picture archive for patients with scoliosis. Overall, there were 7777 AP images and 5621 lateral images.

Synthetically enhanced: unveiling synthetic data's potential in medical imaging research.

Background: Chest X-rays (CXR) are essential for diagnosing a variety of conditions, but when used on new populations, model generalizability issues limit their efficacy. Generative AI, particularly denoising diffusion probabilistic models (DDPMs), offers a promising approach to generating synthetic images, enhancing dataset diversity. This study investigates the impact of synthetic data supplementation on the performance and generalizability of medical imaging research.

Clinical Phenotype and Disease Course of Inflammatory Bowel Disease in Iran: Results of the Iranian Registry of Crohn's and Colitis (IRCC).

Background: Data on the epidemiology of inflammatory bowel disease (IBD) in the Middle East are scarce. We aimed to describe the clinical phenotype, disease course, and medication usage of IBD cases from Iran in the Middle East.

Methods: We conducted a cross-sectional study of registered IBD patients in the Iranian Registry of Crohn's and Colitis (IRCC) from 2017 until 2022.

A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey.

In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tackle clinical challenges underscores the necessity for preparing high-quality medical imaging data. Proper data preparation is crucial as it fosters the creation of standardized and reproducible AI models while minimizing biases.

FDA Review of Radiologic AI Algorithms: Process and Challenges.

A Food and Drug Administration (FDA)-cleared artificial intelligence (AI) algorithm misdiagnosed a finding as an intracranial hemorrhage in a patient, who was finally diagnosed with an ischemic stroke. This scenario highlights a notable failure mode of AI tools, emphasizing the importance of human-machine interaction. In this report, the authors summarize the review processes by the FDA for software as a medical device and the unique regulatory designs for radiologic AI/machine learning algorithms to ensure their safety in clinical practice.

Deep Learning Artificial Intelligence Tool for Automated Radiographic Determination of Posterior Tibial Slope in Patients With ACL Injury.

Background: An increased posterior tibial slope (PTS) corresponds with an increased risk of graft failure after anterior cruciate ligament (ACL) reconstruction (ACLR). Validated methods of manual PTS measurements are subject to potential interobserver variability and can be inefficient on large datasets.

Purpose/hypothesis: To develop a deep learning artificial intelligence technique for automated PTS measurement from standard lateral knee radiographs.