[Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging].

The recent advent of large language models (LLMs), such as ChatGPT, has drawn attention to generative artificial intelligence (AI) in a number of fields. Generative AI can produce different types of data including text, images, and voice, depending on the training methods and datasets used. Additionally, recent advancements in multimodal techniques, which can simultaneously process multiple data types like text and images, have expanded the potential of using multimodal generative AI in the medical environment where various types of clinical and imaging information are used together.

Generative Adversarial Network with Robust Discriminator Through Multi-Task Learning for Low-Dose CT Denoising.

Reducing the dose of radiation in computed tomography (CT) is vital to decreasing secondary cancer risk. However, the use of low-dose CT (LDCT) images is accompanied by increased noise that can negatively impact diagnoses. Although numerous deep learning algorithms have been developed for LDCT denoising, several challenges persist, including the visual incongruence experienced by radiologists, unsatisfactory performances across various metrics, and insufficient exploration of the networks' robustness in other CT domains.

Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals.

The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes different generative AI models and their potential applications in the field of medicine and explores the evolving landscape of Generative Adversarial Networks and diffusion models since the introduction of generative AI models. These models have made valuable contributions to the field of radiology.

Approximating Intermediate Feature Maps of Self-Supervised Convolution Neural Network to Learn Hard Positive Representations in Chest Radiography.

Recent advances in contrastive learning have significantly improved the performance of deep learning models. In contrastive learning of medical images, dealing with positive representation is sometimes difficult because some strong augmentation techniques can disrupt contrastive learning owing to the subtle differences between other standardized CXRs compared to augmented positive pairs; therefore, additional efforts are required. In this study, we propose intermediate feature approximation (IFA) loss, which improves the performance of contrastive convolutional neural networks by focusing more on positive representations of CXRs without additional augmentations.

Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology: A Comprehensive Review of Solutions Beyond Supervised Learning.

Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows.

Screening of adolescent idiopathic scoliosis using generative adversarial network (GAN) inversion method in chest radiographs.

Objective: Conventional computer-aided diagnosis using convolutional neural networks (CNN) has limitations in detecting sensitive changes and determining accurate decision boundaries in spectral and structural diseases such as scoliosis. We devised a new method to detect and diagnose adolescent idiopathic scoliosis in chest X-rays (CXRs) employing the latent space's discriminative ability in the generative adversarial network (GAN) and a simple multi-layer perceptron (MLP) to screen adolescent idiopathic scoliosis CXRs.

Materials And Methods: Our model was trained and validated in a two-step manner.

CheSS: Chest X-Ray Pre-trained Model via Self-supervised Contrastive Learning.

Training deep learning models on medical images heavily depends on experts' expensive and laborious manual labels. In addition, these images, labels, and even models themselves are not widely publicly accessible and suffer from various kinds of bias and imbalances. In this paper, chest X-ray pre-trained model via self-supervised contrastive learning (CheSS) was proposed to learn models with various representations in chest radiographs (CXRs).

Diagnosis of Scoliosis Using Chest Radiographs with a Semi-Supervised Generative Adversarial Network.

Purpose: To develop and validate a deep learning-based screening tool for the early diagnosis of scoliosis using chest radiographs with a semi-supervised generative adversarial network (GAN).

Materials And Methods: Using a semi-supervised learning framework with a GAN, a screening tool for diagnosing scoliosis was developed and validated through the chest PA radiographs of patients at two different tertiary hospitals. Our proposed method used training GAN with mild to severe scoliosis only in a semi-supervised manner, as an upstream task to learn scoliosis representations and a downstream task to perform simple classification for differentiating between normal and scoliosis states sensitively.

Improved performance and robustness of multi-task representation learning with consistency loss between pretexts for intracranial hemorrhage identification in head CT.

With the recent development of deep learning, the classification and segmentation tasks of computer-aided diagnosis (CAD) using non-contrast head computed tomography (NCCT) for intracranial hemorrhage (ICH) has become popular in emergency medical care. However, a few challenges remain, such as the difficulty of training due to the heterogeneity of ICH, the requirement for high performance in both sensitivity and specificity, patient-level predictions demanding excessive costs, and vulnerability to real-world external data. In this study, we proposed a supervised multi-task aiding representation transfer learning network (SMART-Net) for ICH to overcome these challenges.

Clinical Impact of a Quality Improvement Program Including Dedicated Emergency Radiology Personnel on Emergency Surgical Management: A Propensity Score-Matching Study.

Objective: To investigate the clinical impact of a quality improvement program including dedicated emergency radiology personnel (QIP-DERP) on the management of emergency surgical patients in the emergency department (ED).

Materials And Methods: This retrospective study identified all adult patients (n = 3667) who underwent preoperative body CT, for which written radiology reports were generated, and who subsequently underwent non-elective surgery between 2007 and 2018 in the ED of a single urban academic tertiary medical institution. The study cohort was divided into periods before and after the initiation of QIP-DERP.

Emergency triage of brain computed tomography via anomaly detection with a deep generative model.

Triage is essential for the early diagnosis and reporting of neurologic emergencies. Herein, we report the development of an anomaly detection algorithm (ADA) with a deep generative model trained on brain computed tomography (CT) images of healthy individuals that reprioritizes radiology worklists and provides lesion attention maps for brain CT images with critical findings. In the internal and external validation datasets, the ADA achieved area under the curve values (95% confidence interval) of 0.

Patient recalls associated with resident-to-attending radiology report discrepancies: predictive factors for risky discrepancies.

Background: This study aimed to identify predictive factors for risky discrepancies in the emergency department (ED) by analyzing patient recalls associated with resident-to-attending radiology report discrepancies (RRDs).

Results: This retrospective study analyzed 759 RRDs in computed tomography (CT) and magnetic resonance imaging and their outcomes from 2013 to 2021. After excluding 73 patients lost to follow-up, we included 686 records in the final analysis.

Bone suppression on pediatric chest radiographs via a deep learning-based cascade model.

Background And Objective: Bone suppression images (BSIs) of chest radiographs (CXRs) have been proven to improve diagnosis of pulmonary diseases. To acquire BSIs, dual-energy subtraction (DES) or a deep-learning-based model trained with DES-based BSIs have been used. However, neither technique could be applied to pediatric patients owing to the harmful effects of DES.

Impact of diagnostic errors on adverse outcomes: learning from emergency department revisits with repeat CT or MRI.

Background: To investigate diagnostic errors and their association with adverse outcomes (AOs) during patient revisits with repeat imaging (RVRIs) in the emergency department (ED).

Results: Diagnostic errors stemming from index imaging studies and AOs within 30 days in 1054 RVRIs (≤ 7 days) from 2005 to 2015 were retrospectively analyzed according to revisit timing (early [≤ 72 h] or late [> 72 h to 7 days] RVRIs). Risk factors for AOs were assessed using multivariable logistic analysis.

Value of bone suppression software in chest radiographs for improving image quality and reducing radiation dose.

Objectives: To compare image quality and radiation dose between dual-energy subtraction (DES)-based bone suppression images (D-BSIs) and software-based bone suppression images (S-BSIs).

Methods: Chest radiographs (CXRs) of forty adult patients were obtained with the two X-ray devices, one with DES and one with bone suppression software. Three image quality metrics (relative mean absolute error (RMAE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM)) between original CXR and BSI for each of D-BSI and S-SBI groups were calculated for each bone and soft tissue areas.

Assessment of naive indolent lymphoma using whole-body diffusion-weighted imaging and T2-weighted MRI: results of a prospective study in 30 patients.

Background: We prospectively evaluated the diagnostic utility of whole-body diffusion-weighted imaging with background body signal suppression and T2-weighted short-tau inversion recovery MRI (WB-DWIBS/STIR) for the pretherapeutic staging of indolent lymphoma in 30 patients.

Methods: This prospective study included 30 treatment-naive patients with indolent lymphomas who underwent WB-DWIBS/STIR and conventional imaging workup plus biopsy. The pretherapeutic staging agreement, sensitivity, and specificity of WB-DWIBS/STIR were investigated with reference to the multimodality and multidisciplinary consensus review for nodal and extranodal lesions excluding bone marrow.

Ischemic colitis after enema administration: Incidence, timing, and clinical features.

Background: Enema administration is a common procedure in the emergency department (ED). However, several published case reports on enema-related ischemic colitis (IC) have raised the concerns regarding the safety of enema agents. Nevertheless, information on its true incidence and characteristics are still lacking.

Added Value of Bone Suppression Image in the Detection of Subtle Lung Lesions on Chest Radiographs with Regard to Reader's Expertise.

Background: Chest radiographs (CXR) are the most commonly used imaging techniques by various clinicians and radiologists. However, detecting lung lesions on CXR depends largely on the reader's experience level, so there have been several trials to overcome this problem using post-processing of CXR. We investigated the added value of bone suppression image (BSI) in detecting various subtle lung lesions on CXR with regard to reader's expertise.

Early fragment removal on fertilization day 2 significantly improves the subsequent development and clinical outcomes of fragmented human embryos.

Objective: To determine whether fragment removal on fertilization (IVF) day 2 improved the subsequent development and pregnancy outcomes of fragmented embryos compared to similar-grade embryos without fragment removal.

Methods: This study was a retrospective analysis involving 191 IVF cycles in which all embryos had over 10% fragmentation (grade 3 or 4) on day 2 of the IVF-embryo transfer cycle from March 2015 to December 2017. IVF cycles were divided into the fragment removal group (n=87) and the no fragment removal group (n=104) as a control cohort.

Intraductal Papillary Neoplasm of the Bile Duct: Clinical, Imaging, and Pathologic Features.

Objective: We outline the concept of intraductal papillary neoplasm of the bile duct (IPNB), discuss the morphologic features of IPNB and the differential diagnoses, and describe the radiologic approaches used in multidisciplinary management.

Conclusion: The concept of IPNB has been evolving. Because the imaging features of IPNB can be variable, different mimickers according to IPNB subtype can be considered.

Assessment of pulmonary arterial enhancement on CT pulmonary angiography using a leg vein for contrast media administration.

The purpose of our study was to compare pulmonary artery (PA) enhancement according to venous routes of contrast media (CM) administration in patients who underwent CT pulmonary angiography (CTPA) in the emergency department (ED).This retrospective study reviewed the CTPAs of 24 patients who administered CM via leg veins (group A) and 72 patients via arm veins (group B) with age and gender matching at a ratio of 1:3. Clinical data, aorta attenuation (Aoatten), and PA attenuation (PAatten) were compared between group A and B.