Medical Imaging Research Articles

The American Society of Radiologic Technologists (ASRT) AI educator survey: A cross-sectional study to explore knowledge, experience, and use of AI within education

IntroductionArtificial Intelligence (AI) is revolutionizing medical imaging and radiation therapy. AI-powered applications are being deployed to aid Medical Radiation Technologists (MRTs) in clinical workflows, decision-making, dose optimisation, and a wide range of other tasks. Exploring the levels of AI education provided across the United States is crucial to prepare future graduates to deliver the digital future. This study aims to assess educators’ levels of AI knowledge, the current state of AI educational provisions, the perceived challenges around AI education, and important factors for future advancements. MethodsAn online survey was electronically administered to all radiologic technologists in the American Society of Radiologic Technologists (ASRT) database who indicated that they had an educator role in the United States. This was distributed through the membership of the ASRT, from February to April 2023. All quantitative data was analysed using frequency and descriptive statistics. The survey's open-ended questions were analysed using a conceptual content analysis approach. ResultsOut of 5,066 educators in the ASRT database, 373 valid responses were received, resulting in a response rate of 7.4%. Despite 84.5% of educators expressing the importance of teaching AI, 23.7% currently included AI in academic curricula. Of the 76.3% that did not include AI in their curricula, lack of AI knowledge among educators was the top reason for not integrating AI in education (59.1%). Similarly, AI-enabled tools were utilised by only 11.1% of the programs to assist teaching. The levels of trust in AI varied among educators. ConclusionThe study found that although US educators of MRTs have a good baseline knowledge of general concepts regarding AI, they could improve on the teaching and use of AI in their curricula. AI training and guidance, adequate time to develop educational resources, and funding and support from higher education institutions were key priorities as highlighted by educators.

Building RadiologyNET: an unsupervised approach to annotating a large-scale multimodal medical database

BackgroundThe use of machine learning in medical diagnosis and treatment has grown significantly in recent years with the development of computer-aided diagnosis systems, often based on annotated medical radiology images. However, the lack of large annotated image datasets remains a major obstacle, as the annotation process is time-consuming and costly. This study aims to overcome this challenge by proposing an automated method for annotating a large database of medical radiology images based on their semantic similarity.ResultsAn automated, unsupervised approach is used to create a large annotated dataset of medical radiology images originating from the Clinical Hospital Centre Rijeka, Croatia. The pipeline is built by data-mining three different types of medical data: images, DICOM metadata and narrative diagnoses. The optimal feature extractors are then integrated into a multimodal representation, which is then clustered to create an automated pipeline for labelling a precursor dataset of 1,337,926 medical images into 50 clusters of visually similar images. The quality of the clusters is assessed by examining their homogeneity and mutual information, taking into account the anatomical region and modality representation.ConclusionsThe results indicate that fusing the embeddings of all three data sources together provides the best results for the task of unsupervised clustering of large-scale medical data and leads to the most concise clusters. Hence, this work marks the initial step towards building a much larger and more fine-grained annotated dataset of medical radiology images.

Integrating Clinical Data and Medical Imaging in Lung Cancer: Feasibility Study Using the Observational Medical Outcomes Partnership Common Data Model Extension.

Digital transformation, particularly the integration of medical imaging with clinical data, is vital in personalized medicine. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardizes health data. However, integrating medical imaging remains a challenge. This study proposes a method for combining medical imaging data with the OMOP CDM to improve multimodal research. Our approach included the analysis and selection of digital imaging and communications in medicine header tags, validation of data formats, and alignment according to the OMOP CDM framework. The Fast Healthcare Interoperability Resources ImagingStudy profile guided our consistency in column naming and definitions. Imaging Common Data Model (I-CDM), constructed using the entity-attribute-value model, facilitates scalable and efficient medical imaging data management. For patients with lung cancer diagnosed between 2010 and 2017, we introduced 4 new tables-IMAGING_STUDY, IMAGING_SERIES, IMAGING_ANNOTATION, and FILEPATH-to standardize various imaging-related data and link to clinical data. This framework underscores the effectiveness of I-CDM in enhancing our understanding of lung cancer diagnostics and treatment strategies. The implementation of the I-CDM tables enabled the structured organization of a comprehensive data set, including 282,098 IMAGING_STUDY, 5,674,425 IMAGING_SERIES, and 48,536 IMAGING_ANNOTATION records, illustrating the extensive scope and depth of the approach. A scenario-based analysis using actual data from patients with lung cancer underscored the feasibility of our approach. A data quality check applying 44 specific rules confirmed the high integrity of the constructed data set, with all checks successfully passed, underscoring the reliability of our findings. These findings indicate that I-CDM can improve the integration and analysis of medical imaging and clinical data. By addressing the challenges in data standardization and management, our approach contributes toward enhancing diagnostics and treatment strategies. Future research should expand the application of I-CDM to diverse disease populations and explore its wide-ranging utility for medical conditions.

Medical imaging in cancer cachexia.

Cancer cachexia, often referred to as "wasting syndrome," is characterized by fatigue, weakness, and involuntary weight loss. This syndrome is concomitant with progressive skeletal muscle atrophy with or without adipose tissue loss and is frequently accompanied by systemic inflammation. Understanding the complexities of cancer cachexia is crucial for early detection and intervention, and it is also paramount for enhancing patient outcomes. Medical imaging, comprising diverse imaging modalities, plays apivotal role in this context, facilitating the diagnosis and surveillance assessment of both the disease extent and the body composition changes that offer valuable information and insights into disease progression. This article provides acomprehensive discourse of the pathophysiological mechanisms and clinical manifestations of cancer cachexia as well as the role of medical imaging in this setting. Particular emphasis is placed on contemporary multidisciplinary and translational research efforts for the development of diagnostic and treatment tools, aiming to mitigate the devastating consequences of cancer cachexia.

Block-based optimization for enhancing reversible watermarking using reduce difference expansion

In recent years, reversible watermarking has emerged as a promising technique that safely embeds data in digital images without compromising their originality. This method is particularly useful for sensitive images such as military, art, and medical images, where each pixel contains important information requiring authentication. Researchers have been attempting to develop this method further to increase payload capacity while maintaining visual quality and low computational complexity. In this study, we developed a reversible watermarking with block-based optimization based on Reduced Difference Expansion (RDE) applied to 3×3 pixel blocks, allowing for the embedding of 8?bit data. Based on experimental results from tests conducted on 2 common images and 3 medical images, our method could consistently achieve a payload capacity of up to 0.8924 bpp with a PSNR of 41.077 dB while maintaining good visual quality across various image categories, outperforming previous approaches.

Efficient musculoskeletal annotation using free-form deformation

Traditionally, constructing training datasets for automatic muscle segmentation from medical images involved skilled operators, leading to high labor costs and limited scalability. To address this issue, we developed a tool that enables efficient annotation by non-experts and assessed its effectiveness for training an automatic segmentation network. Our system allows users to deform a template three-dimensional (3D) anatomical model to fit a target magnetic-resonance image using free-form deformation with independent control points for axial, sagittal, and coronal directions. This method simplifies the annotation process by allowing non-experts to intuitively adjust the model, enabling simultaneous annotation of all muscles in the template. We evaluated the quality of the tool-assisted segmentation performed by non-experts, which achieved a Dice coefficient greater than 0.75 compared to expert segmentation, without significant errors such as mislabeling adjacent muscles or omitting musculature. An automatic segmentation network trained with datasets created using this tool demonstrated performance comparable to or superior to that of networks trained with expert-generated datasets. This innovative tool significantly reduces the time and labor costs associated with dataset creation for automatic muscle segmentation, potentially revolutionizing medical image annotation and accelerating the development of deep learning-based segmentation networks in various clinical applications.

Rediscovering extra-axial collections on medical imaging: subdural lymphatic hygroma.

Rediscovering extra-axial collections on medical imaging: subdural lymphatic hygroma.

ChatGPT's diagnostic performance based on textual vs. visual information compared to radiologists' diagnostic performance in musculoskeletal radiology.

To compare the diagnostic accuracy of Generative Pre-trained Transformer (GPT)-4-based ChatGPT, GPT-4 with vision (GPT-4V) based ChatGPT, and radiologists in musculoskeletal radiology. We included 106 "Test Yourself" cases from Skeletal Radiology between January 2014 and September 2023. We input the medical history and imaging findings into GPT-4-based ChatGPT and the medical history and images into GPT-4V-based ChatGPT, then both generated a diagnosis for each case. Two radiologists (a radiology resident and a board-certified radiologist) independently provided diagnoses for all cases. The diagnostic accuracy rates were determined based on the published ground truth. Chi-square tests were performed to compare the diagnostic accuracy of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and radiologists. GPT-4-based ChatGPT significantly outperformed GPT-4V-based ChatGPT (p < 0.001) with accuracy rates of 43% (46/106) and 8% (9/106), respectively. The radiology resident and the board-certified radiologist achieved accuracy rates of 41% (43/106) and 53% (56/106). The diagnostic accuracy of GPT-4-based ChatGPT was comparable to that of the radiology resident, but was lower than that of the board-certified radiologist although the differences were not significant (p = 0.78 and 0.22, respectively). The diagnostic accuracy of GPT-4V-based ChatGPT was significantly lower than those of both radiologists (p < 0.001 and < 0.001, respectively). GPT-4-based ChatGPT demonstrated significantly higher diagnostic accuracy than GPT-4V-based ChatGPT. While GPT-4-based ChatGPT's diagnostic performance was comparable to radiology residents, it did not reach the performance level of board-certified radiologists in musculoskeletal radiology. GPT-4-based ChatGPT outperformed GPT-4V-based ChatGPT and was comparable to radiology residents, but it did not reach the level of board-certified radiologists in musculoskeletal radiology. Radiologists should comprehend ChatGPT's current performance as a diagnostic tool for optimal utilization. This study compared the diagnostic performance of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and radiologists in musculoskeletal radiology. GPT-4-based ChatGPT was comparable to radiology residents, but did not reach the level of board-certified radiologists. When utilizing ChatGPT, it is crucial to input appropriate descriptions of imaging findings rather than the images.

Fair AI-powered orthopedic image segmentation: addressing bias and promoting equitable healthcare

AI-powered segmentation of hip and knee bony anatomy has revolutionized orthopedics, transforming pre-operative planning and post-operative assessment. Despite the remarkable advancements in AI algorithms for medical imaging, the potential for biases inherent within these models remains largely unexplored. This study tackles these concerns by thoroughly re-examining AI-driven segmentation for hip and knee bony anatomy. While advanced imaging modalities like CT and MRI offer comprehensive views, plain radiographs (X-rays) predominate the standard initial clinical assessment due to their widespread availability, low cost, and rapid acquisition. Hence, we focused on plain radiographs to ensure the utilization of our contribution in diverse healthcare settings, including those with limited access to advanced imaging technologies. This work provides insights into the underlying causes of biases in AI-based knee and hip image segmentation through an extensive evaluation, presenting targeted mitigation strategies to alleviate biases related to sex, race, and age, using an automatic segmentation that is fair, impartial, and safe in the context of AI. Our contribution can enhance inclusivity, ethical practices, equity, and an unbiased healthcare environment with advanced clinical outcomes, aiding decision-making and osteoarthritis research. Furthermore, we have made all the codes and datasets publicly and freely accessible to promote open scientific research.

Integrating metaheuristics and artificial intelligence for healthcare: basics, challenging and future directions

Accurate and rapid disease detection is necessary to manage health problems early. Rapid increases in data amount and dimensionality caused challenges in many disciplines, with the primary issues being high computing costs, memory costs, and low accuracy performance. These issues will arise since Machine Learning (ML) classifiers are mostly used in these fields. However, noisy and irrelevant features have an impact on ML accuracy. Therefore, to choose the best subset of features and decrease the dimensionality of the data, Metaheuristics (MHs) optimization algorithms are applied to Feature Selection (FS) using various modalities of medical imaging or disease datasets with different dimensions. The review starts by giving a general overview of the many approaches to AI algorithms, followed by a general overview of the various MH algorithms for healthcare applications, an analysis of MHs boosted AI for healthcare applications, and using a wide range of research databases as a data source for access to numerous field publications. The final section of this review discusses the problems and challenges facing healthcare application development.

Distance Education through the Perspective of Students of Anesthetic Technician, Emergency Medical Technician, and Medical Imaging Technician Programs

Aim: The study aimed to evaluate the distance education process through the perspective of associate degree programs and to identify crucial issues for potential future pandemics. Methods: The study group (n=166) consisted of associate degree students of the Anesthetic Technician, Emergency Medical Technician, and Medical Imaging Technician Programs. Data were collected through an electronic questionnaire regarding main headings: 'Theoretical Courses, Practical Courses, and Professional Development', 'Course Participation and Motivation', 'Measurement and Evaluation', and 'Student Suggestions for Distance Education'. Questions and statements were scored by 5-point Likert. Results: When the effects of distance education ‘on the level of internalization of the profession’ and ‘on the effect on professional competencies’ were assessed, the scores of Medical Imaging Technician Program students were lower than the scores of Anesthetic Technician Program students and Emergency Medical Technician Program students (p

Empowered chaotic local search-based differential evolution algorithm with entropy-based hybrid objective function for brain tumor segmentation

In neuro-oncology, the precise segmentation of brain tumors from Magnetic Resonance Images is crucial for diagnosis, treatment planning, and monitoring disease progression. Accurate segmentation helps determine the tumor’s size, location, and growth potential, which is essential for formulating effective treatment strategies. In response to this challenge, we developed a novel approach using Chaotic Local Search-Enhanced Differential Evolution (CJADE). CJADE, particularly its variant CJADE-M, which employs chaotic maps selected through a probability-based approach, has proven effective in optimizing brain tumor segmentation. Our study shows that CJADE-M outperforms traditional metaheuristic algorithms on various evaluation metrics. We further enhanced CJADE-M with an entropy-based hybrid objective function, which improved accuracy and reduced computational time in tumor segmentation compared to conventional methods like Minimum Cross-Entropy and Kapur. This makes our method suitable for real-time medical imaging analysis. Our findings indicate that CJADE-M, equipped with the hybrid objective function, achieves superior segmentation performance for both benign lobulated and malignant irregular tumors across metrics such as PSNR, FSIM, QILV, and HPSI. By providing a more accurate and efficient tool, our approach can significantly enhance the outcomes of brain tumor diagnosis and treatment, improving patient care in neuro-oncology.

Advancing Medical Imaging Research Through Standardization: The Path to Rapid Development, Rigorous Validation, and Robust Reproducibility.

Artificial intelligence (AI) has made significant advances in radiology. Nonetheless, challenges in AI development, validation, and reproducibility persist, primarily due to the lack of high-quality, large-scale, standardized data across the world. Addressing these challenges requires comprehensive standardization of medical imaging data and seamless integration with structured medical data.Developed by the Observational Health Data Sciences and Informatics community, the OMOP Common Data Model enables large-scale international collaborations with structured medical data. It ensures syntactic and semantic interoperability, while supporting the privacy-protected distribution of research across borders. The recently proposed Medical Imaging Common Data Model is designed to encompass all DICOM-formatted medical imaging data and integrate imaging-derived features with clinical data, ensuring their provenance.The harmonization of medical imaging data and its seamless integration with structured clinical data at a global scale will pave the way for advanced AI research in radiology. This standardization will enable federated learning, ensuring privacy-preserving collaboration across institutions and promoting equitable AI through the inclusion of diverse patient populations. Moreover, it will facilitate the development of foundation models trained on large-scale, multimodal datasets, serving as powerful starting points for specialized AI applications. Objective and transparent algorithm validation on a standardized data infrastructure will enhance reproducibility and interoperability of AI systems, driving innovation and reliability in clinical applications.

“Don’t Panic” – Stephen Aylward, PhD, on AI in Medical Imaging

“Don’t Panic” – Stephen Aylward, PhD, on AI in Medical Imaging

Long Non-Coding RNAs and Alzheimer's Disease: Towards Personalized Diagnosis.

Alzheimer's disease (AD), a neurodegenerative disorder characterized by progressive cognitive decline, is the most common form of dementia. Currently, there is no single test that can diagnose AD, especially in understudied populations and developing countries. Instead, diagnosis is based on a combination of medical history, physical examination, cognitive testing, and brain imaging. Exosomes are extracellular nanovesicles, primarily composed of RNA, that participate in physiological processes related to AD pathogenesis such as cell proliferation, immune response, and neuronal and cardiovascular function. However, the identification and understanding of the potential role of long non-coding RNAs (lncRNAs) in AD diagnosis remain largely unexplored. Here, we clinically, cognitively, and genetically characterized a sample of 15 individuals diagnosed with AD (cases) and 15 controls from Barranquilla, Colombia. Advanced bioinformatics, analytics and Machine Learning (ML) techniques were used to identify lncRNAs differentially expressed between cases and controls. The expression of 28,909 lncRNAs was quantified. Of these, 18 were found to be differentially expressed and harbored in pivotal genes related to AD. Two lncRNAs, ENST00000608936 and ENST00000433747, show promise as diagnostic markers for AD, with ML models achieving > 95% sensitivity, specificity, and accuracy in both the training and testing datasets. These findings suggest that the expression profiles of lncRNAs could significantly contribute to advancing personalized AD diagnosis in this community, offering promising avenues for early detection and follow-up.

FreqSNet: a multiaxial integration of frequency and spatial domains for medical image segmentation

Objective. In recent years, convolutional neural networks, which typically focus on extracting spatial domain features, have shown limitations in learning global contextual information. However, frequency domain can offer a global perspective that spatial domain methods often struggle to capture. To address this limitation, we propose FreqSNet, which leverages both frequency and spatial features for medical image segmentation. Approach. To begin, we propose a frequency-space representation aggregation block (FSRAB) to replace conventional convolutions. FSRAB contains three frequency domain branches to capture global frequency information along different axial combinations, while a convolutional branch is designed to interact information across channels in local spatial features. Secondly, the multiplex expansion attention block extracts long-range dependency information using dilated convolutional blocks, while suppressing irrelevant information via attention mechanisms. Finally, the introduced Feature Integration Block enhances feature representation by integrating semantic features that fuse spatial and channel positional information. Main results. We validated our method on 5 public datasets, including BUSI, CVC-ClinicDB, CVC-ColonDB, ISIC-2018, and Luna16. On these datasets, our method achieved Intersection over Union (IoU) scores of 75.46%, 87.81%, 79.08%, 84.04%, and 96.99%, and Hausdorff distance values of 22.22 mm, 13.20 mm, 13.08 mm, 13.51 mm, and 5.22 mm, respectively. Compared to other state-of-the-art methods, our FreqSNet achieves better segmentation results. Significance. Our method can effectively combine frequency domain information with spatial domain features, enhancing the segmentation performance and generalization capability in medical image segmentation tasks.

R2C-GAN: Restore-to-Classify Generative Adversarial Networks for blind X-ray restoration and COVID-19 classification

Restoration of poor-quality medical images with a blended set of artifacts plays a vital role in a reliable diagnosis. As a pioneer study in blind X-ray restoration, we propose a joint model for generic image restoration and classification: Restore-to-Classify Generative Adversarial Networks (R2C-GANs). This is the first generic restoration approach forming an Image-to-Image translation task from poor-quality having noisy, blurry, or over/under-exposed images to high-quality image domain where forward and inverse transformations are learned using unpaired training samples. Simultaneously, the joint classification preserves the diagnostic-related label during restoration. Each R2C-GAN is equipped with operational layers/neurons in a compact architecture. The proposed joint model successfully restores images while achieving state-of-the-art Coronavirus Disease 2019 (COVID-19) classification with above 90% in F1-Score. In qualitative analysis, the restoration performance is confirmed by medical doctors where 68% of the restored images are selected against the original images. We share the software implementation at https://github.com/meteahishali/R2C-GAN.

Histopathology imaging and clinical data including remission status in pediatric inflammatory bowel disease

The incidence of inflammatory bowel disease (IBD) is increasing annually. Children with IBD often suffer significant morbidity due to physical and emotional effects of the disease and treatment. Corticosteroids, often a component of therapy, carry undesirable side effects with long term use. Steroid-free remission has become a standard for care-quality improvement. Anticipating therapeutic outcomes is difficult, with treatments often leveraged in a trial-and-error fashion. Artificial intelligence (AI) has demonstrated success in medical imaging classification tasks. Predicting patients who will attain remission will help inform treatment decisions. The provided dataset comprises 951 tissue section scans (167 whole-slides) obtained from 18 pediatric IBD patients. Patient level structured data include IBD diagnosis, 12- and 52-week steroid use and name, and remission status. Each slide is labelled with biopsy site and normal or abnormal classification per the surgical pathology report. Each tissue section scan from an abnormal slide is further classified by an experienced pathologist. Researchers utilizing this dataset may select from the provided outcomes or add labels and annotations from their own institutions.

Shielding sensitive medical imaging data

Shielding sensitive medical imaging data

Medical imaging in melioidosis - 20-year experience in a non-endemic Australian city.

Melioidosis may occasionally be encountered in non-endemic areas and medical imaging is frequently used to identify and characterise sites of disease. The purpose of this study is to describe the spectrum of imaging findings encountered in melioidosis patients treated in the tertiary public hospitals of Perth, Western Australia, between 2002 and 2022. A database search and electronic medical record review was used to identify cases. Cases were included if they had Burkholderia pseudomallei isolated on culture and if they had at least one diagnostic imaging study performed at a Perth public tertiary hospital. The relevant imaging studies were reviewed, and imaging findings were recorded. Thirty-six cases were identified. The most common disease manifestation was bacteraemia (72%, 26 cases), followed by pulmonary infection (58%, 21 cases), skin and soft tissue infection (22%, eight cases), prostate abscess (14%, five cases) and septic arthritis (6%, two cases). A previously unreported case of isolated melioid pleural effusion was identified, as was a case of reactivated chronic latent pulmonary melioidosis with an apparent delay of over 20 years between the onset of symptoms and the time of infection. In cases with pulmonary melioidosis, the major lung abnormalities on CT chest could be categorised into one of two distinct patterns: nodular-predominant (78%) or consolidation-predominant (22%). Further research is required to assess the utility of the pattern-based categorisation of lung abnormalities on CT chest seen in the pulmonary melioidosis cases of this series.