Artificial Intelligence In Medical Imaging Research Articles

Clinical Applications of Artificial Intelligence in Medical Imaging and Image Processing—A Review

Clinical Applications of Artificial Intelligence in Medical Imaging and Image Processing—A Review

Navigating the ethical landscape of artificial intelligence in radiography: a cross-sectional study of radiographers’ perspectives

BackgroundThe integration of artificial intelligence (AI) in radiography presents transformative opportunities for diagnostic imaging and introduces complex ethical considerations. The aim of this cross-sectional study was to explore radiographers’ perspectives on the ethical implications of AI in their field and identify key concerns and potential strategies for addressing them.MethodsA structured questionnaire was distributed to a diverse group of radiographers in Saudi Arabia. The questionnaire included items on ethical concerns related to AI, the perceived impact on clinical practice, and suggestions for ethical AI integration in radiography. The data were analyzed using quantitative and qualitative methods to capture a broad range of perspectives.ResultsThree hundred eighty-eight radiographers responded and had varying levels of experience and specializations. Most (44.8%) participants were unfamiliar with the integration of AI into radiography. Approximately 32.9% of radiographers expressed uncertainty regarding the importance of transparency and explanatory capabilities in the AI systems used in radiology. Many (36.9%) participants indicated that they believed that AI systems used in radiology should be transparent and provide justifications for their decision-making procedures. A significant preponderance (44%) of respondents agreed that implementing AI in radiology may increase ethical dilemmas. However, 27.8%expressed uncertainty in recognizing and understanding the potential ethical issues that could arise from integrating AI in radiology. Of the respondents, 41.5% stated that the use of AI in radiology required establishing specific ethical guidelines. However, a significant percentage (28.9%) expressed the opposite opinion, arguing that utilizing AI in radiology does not require adherence to ethical standards. In contrast to the 46.6% of respondents voicing concerns about patient privacy over AI implementation, 41.5% of respondents did not have any such apprehensions.ConclusionsThis study revealed a complex ethical landscape in the integration of AI in radiography, characterized by enthusiasm and apprehension among professionals. It underscores the necessity for ethical frameworks, education, and policy development to guide the implementation of AI in radiography. These findings contribute to the ongoing discourse on AI in medical imaging and provide insights that can inform policymakers, educators, and practitioners in navigating the ethical challenges of AI adoption in healthcare.

Invited Commentary: The Double-edged Sword of Bias in Medical Imaging Artificial Intelligence.

Invited Commentary: The Double-edged Sword of Bias in Medical Imaging Artificial Intelligence.

AI and machine learning in medical imaging: key points from development to translation

Abstract Innovation in medical imaging artificial intelligence (AI)/machine learning (ML) demands extensive data collection, algorithmic advancements, and rigorous performance assessments encompassing aspects such as generalizability, uncertainty, bias, fairness, trustworthiness, and interpretability. Achieving widespread integration of AI/ML algorithms into diverse clinical tasks will demand a steadfast commitment to overcoming issues in model design, development, and performance assessment. The complexities of AI/ML clinical translation present substantial challenges, requiring engagement with relevant stakeholders, assessment of cost-effectiveness for user and patient benefit, timely dissemination of information relevant to robust functioning throughout the AI/ML lifecycle, consideration of regulatory compliance, and feedback loops for real-world performance evidence. This commentary addresses several hurdles for the development and adoption of AI/ML technologies in medical imaging. Comprehensive attention to these underlying and often subtle factors is critical not only for tackling the challenges but also for exploring novel opportunities for the advancement of AI in radiology.

CAN ARTIFICIAL INTELLIGENCE ACCURATELY DETECT URINARY STONES? A SYSTEMATIC REVIEW.

To perform a systematic review on artificial intelligence(AI) performances to detect urinary stones. A PROSPERO-registered(CRD473152) systematic search of Scopus, Web of Science, Embase and PubMed databases was performed to identify original research articles pertaining to AI stone detection or measurement, using search terms("automatic" OR "machine learning" OR "convolutional neural network" OR "artificial intelligence" OR "detection" AND "stone volume"). Risk-of-bias(RoB) assessment was performed according to the Cochrane RoB tool, the Joanna Briggs Institute Checklist for nonrandomized studies and the Checklist for Artificial Intelligence in Medical Imaging(CLAIM). Twelve studies were selected for final review, including 3 multicenter and 9 single-center retrospective studies. 11 studies completed at least 50% of the CLAIM checkpoints and only one presented a high ROB. All included studies aimed to detect on kidney(5/12, 42%), ureter(2/12, 16%) or urinary(5/12, 42%) stones on Non-Contrast Computed Tomography(NCCT), but 42% intended to automate measurement. Stone distinction from vascular calcification interested 2 studies. All studies used AI machine learning network training and internal validation, but a single one provided an external validation. Trained networks achieved stone detection with sensitivity, specificity and accuracy rates ranging from 58,.7 to 100%, 68,.5 to 100% and 63 to 99,.95%, respectively. Detection Dice score ranged from 83% to 97%. A high correlation between manual and automated Stone Volume(r=0,.95) was noted. Differentiate distal ureteral stones and phleboliths seemed feasible. Artificial Intelligence processes can achieve automated urinary stone detection from NCCT. Further studies should provide urinary stones detection coupled with phlebolith distinction and an external validation, and include anatomical abnormalities and urological foreign bodies (ureteral stent and nephrostomy tubes) cases.

Advancements in Fundus Image Analysis: A Comprehensive Method of AI-Based Classification and Segmentation Technique

Medical imaging is a vital tool in the existing healthcare system for precise analysis and treatment of various medical conditions. The use of artificial intelligence particularly machine learning and deep learning approaches has changed the interpretation of medical images and resulted in substantial advances in the area. With the increasing occurrence of eye diseases and the imperative need for early diagnosis, artificial intelligence presents potential solutions for the automatic, precise, and early detection of various eye problems. New advances in machine learning and deep learning allow different ocular disorders to be automatically analyzed, classified, and segmented which leads to early and precise diagnosis. This study attempts to give a comprehensive overview of the rapidly evolving field by reviewing the most recent approaches, challenges, and possible uses of artificial intelligence in medical imaging for ocular illnesses with a focus on segmentation and classification. The research also presents advanced methodologies such as transfer learning with MobileNet and UNet for automating the diagnosis of various ocular problems using classification and segmentation. The study highlights the importance of early and accurate diagnoses for better patient outcomes.

Evaluating Biases and Quality Issues in Intermodality Image Translation Studies for Neuroradiology: A Systematic Review.

Intermodality image-to-image translation is an artificial intelligence technique for generating one technique from another. This review was designed to systematically identify and quantify biases and quality issues preventing validation and clinical application of artificial intelligence models for intermodality image-to-image translation of brain imaging. PubMed, Scopus, and IEEE Xplore were searched through August 2, 2023, for artificial intelligence-based image translation models of radiologic brain images. This review collected 102 works published between April 2017 and August 2023. Eligible studies were evaluated for quality using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and for bias using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Medically-focused article adherence was compared with that of engineering-focused articles overall with the Mann-Whitney U test and for each criterion using the Fisher exact test. Median adherence to the relevant CLAIM criteria was 69% and 38% for PROBAST questions. CLAIM adherence was lower for engineering-focused articles compared with medically-focused articles (65% versus 73%, P < .001). Engineering-focused studies had higher adherence for model description criteria, and medically-focused studies had higher adherence for data set and evaluation descriptions. Our review is limited by the study design and model heterogeneity. Nearly all studies revealed critical issues preventing clinical application, with engineering-focused studies showing higher adherence for the technical model description but significantly lower overall adherence than medically-focused studies. The pursuit of clinical application requires collaboration from both fields to improve reporting.

Transparent medical image AI via an image-text foundation model grounded in medical literature.

Building trustworthy and transparent image-based medical artificial intelligence (AI) systems requires the ability to interrogate data and models at all stages of the development pipeline, from training models to post-deployment monitoring. Ideally, the data and associated AI systems could be described using terms already familiar to physicians, but this requires medical datasets densely annotated with semantically meaningful concepts. In the present study, we present a foundation model approach, named MONET (medical concept retriever), which learns how to connect medical images with text and densely scores images on concept presence to enable important tasks in medical AI development and deployment such as data auditing, model auditing and model interpretation. Dermatology provides a demanding use case for the versatility of MONET, due to the heterogeneity in diseases, skin tones and imaging modalities. We trained MONET based on 105,550 dermatological images paired with natural language descriptions from a large collection of medical literature. MONET can accurately annotate concepts across dermatology images as verified by board-certified dermatologists, competitively with supervised models built on previously concept-annotated dermatology datasets of clinical images. We demonstrate how MONET enables AI transparency across the entire AI system development pipeline, from building inherently interpretable models to dataset and model auditing, including a case study dissecting the results of an AI clinical trial.

Remote sensing image information extraction based on Compensated Fuzzy Neural Network and big data analytics

Medical imaging AI systems and big data analytics have attracted much attention from researchers of industry and academia. The application of medical imaging AI systems and big data analytics play an important role in the technology of content based remote sensing (CBRS) development. Environmental data, information, and analysis have been produced promptly using remote sensing (RS). The method for creating a useful digital map from an image data set is called image information extraction. Image information extraction depends on target recognition (shape and color). For low-level image attributes like texture, Classifier-based Retrieval(CR) techniques are ineffective since they categorize the input images and only return images from the determined classes of RS. The issues mentioned earlier cannot be handled by the existing expertise based on a keyword/metadata remote sensing data service model. To get over these restrictions, Fuzzy Class Membership-based Image Extraction (FCMIE), a technology developed for Content-Based Remote Sensing (CBRS), is suggested. The compensation fuzzy neural network (CFNN) is used to calculate the category label and fuzzy category membership of the query image. Use a basic and balanced weighted distance metric. Feature information extraction (FIE) enhances remote sensing image processing and autonomous information retrieval of visual content based on time-frequency meaning, such as color, texture and shape attributes of images. Hierarchical nested structure and cyclic similarity measure produce faster queries when searching. The experiment’s findings indicate that applying the proposed model can have favorable outcomes for assessment measures, including Ratio of Coverage, average means precision, recall, and efficiency retrieval that are attained more effectively than the existing CR model. In the areas of feature tracking, climate forecasting, background noise reduction, and simulating nonlinear functional behaviors, CFNN has a wide range of RS applications. The proposed method CFNN-FCMIE achieves a minimum range of 4–5% for all three feature vectors, sample mean and comparison precision-recall ratio, which gives better results than the existing classifier-based retrieval model. This work provides an important reference for medical imaging artificial intelligence system and big data analysis.

Diagnostic Accuracy of Artificial Intelligence-Based Algorithms in Automated Detection of Neck of Femur Fracture on a Plain Radiograph: A Systematic Review and Meta-analysis.

To evaluate the diagnostic accuracy of artificial intelligence-based algorithms in identifying neck of femur fracture on a plain radiograph. Systematic review and meta-analysis. PubMed, Web of science, Scopus, IEEE, and the Science direct databases were searched from inception to 30 July 2023. Eligible article types were descriptive, analytical, or trial studies published in the English language providing data on the utility of artificial intelligence (AI) based algorithms in the detection of the neck of the femur (NOF) fracture on plain X-ray. The prespecified primary outcome was to calculate the sensitivity, specificity, accuracy, Youden index, and positive and negative likelihood ratios. Two teams of reviewers (each consisting of two members) extracted the data from available information in each study. The risk of bias was assessed using a mix of the CLAIM (the Checklist for AI in Medical Imaging) and QUADAS-2 (A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies) criteria. Of the 437 articles retrieved, five were eligible for inclusion, and the pooled sensitivity of AIs in diagnosing the fracture NOF was 85%, with a specificity of 87%. For all studies, the pooled Youden index (YI) was 0.73. The average positive likelihood ratio (PLR) was 19.88, whereas the negative likelihood ratio (NLR) was 0.17. The random effects model showed an overall odds of 1.16 (0.84-1.61) in the forest plot, comparing the AI system with those of human diagnosis. The overall heterogeneity of the studies was marginal (I2 = 51%). The CLAIM criteria for risk of bias assessment had an overall >70% score. Artificial intelligence (AI)-based algorithms can be used as a diagnostic adjunct, benefiting clinicians by taking less time and effort in neck of the femur (NOF) fracture diagnosis. PROSPERO CRD42022375449. The online version contains supplementary material available at 10.1007/s43465-024-01130-6.

CHD-CXR: a de-identified publicly available dataset of chest x-ray for congenital heart disease.

Congenital heart disease is a prevalent birth defect, accounting for approximately one-third of major birth defects. The challenge lies in early detection, especially in underdeveloped medical regions where a shortage of specialized physicians often leads to oversight. While standardized chest x-rays can assist in diagnosis and treatment, their effectiveness is limited by subtle cardiac manifestations. However, the emergence of deep learning in computer vision has paved the way for detecting subtle changes in chest x-rays, such as lung vessel density, enabling the detection of congenital heart disease in children. This highlights the need for further investigation. The lack of expert-annotated, high-quality medical image datasets hinders the progress of medical image artificial intelligence. In response, we have released a dataset containing 828 DICOM chest x-ray files from children with diagnosed congenital heart disease, alongside corresponding cardiac ultrasound reports. This dataset emphasizes complex structural characteristics, facilitating the transition from machine learning to machine teaching in deep learning. To ascertain the dataset's applicability, we trained a preliminary model and achieved an area under the receiver operating characteristic curve (ROC 0.85). We provide detailed introductions and publicly available datasets at: https://www.kaggle.com/competitions/congenital-heart-disease.

Artificial Intelligence in Medical Imaging: Analyzing the Performance of ChatGPT and Microsoft Bing in Scoliosis Detection and Cobb Angle Assessment.

Open-source artificial intelligence models (OSAIM) find free applications in various industries, including information technology and medicine. Their clinical potential, especially in supporting diagnosis and therapy, is the subject of increasingly intensive research. Due to the growing interest in artificial intelligence (AI) for diagnostic purposes, we conducted a study evaluating the capabilities of AI models, including ChatGPT and Microsoft Bing, in the diagnosis of single-curve scoliosis based on posturographic radiological images. Two independent neurosurgeons assessed the degree of spinal deformation, selecting 23 cases of severe single-curve scoliosis. Each posturographic image was separately implemented onto each of the mentioned platforms using a set of formulated questions, starting from 'What do you see in the image?' and ending with a request to determine the Cobb angle. In the responses, we focused on how these AI models identify and interpret spinal deformations and how accurately they recognize the direction and type of scoliosis as well as vertebral rotation. The Intraclass Correlation Coefficient (ICC) with a 'two-way' model was used to assess the consistency of Cobb angle measurements, and its confidence intervals were determined using the F test. Differences in Cobb angle measurements between human assessments and the AI ChatGPT model were analyzed using metrics such as RMSEA, MSE, MPE, MAE, RMSLE, and MAPE, allowing for a comprehensive assessment of AI model performance from various statistical perspectives. The ChatGPT model achieved 100% effectiveness in detecting scoliosis in X-ray images, while the Bing model did not detect any scoliosis. However, ChatGPT had limited effectiveness (43.5%) in assessing Cobb angles, showing significant inaccuracy and discrepancy compared to human assessments. This model also had limited accuracy in determining the direction of spinal curvature, classifying the type of scoliosis, and detecting vertebral rotation. Overall, although ChatGPT demonstrated potential in detecting scoliosis, its abilities in assessing Cobb angles and other parameters were limited and inconsistent with expert assessments. These results underscore the need for comprehensive improvement of AI algorithms, including broader training with diverse X-ray images and advanced image processing techniques, before they can be considered as auxiliary in diagnosing scoliosis by specialists.

Diagnostic accuracy using artificial intelligence and ultrasound for rotator cuff pathology: A narrative review

AbstractIntroductionThis narrative review aims to assess and compare the diagnostic accuracy of artificial intelligence (AI) techniques with ultrasound in the context of diagnosing rotator cuff pathology.MethodsA comprehensive search of electronic databases was conducted to identify relevant studies published from 2013 to 2023.Studies compared the diagnostic accuracy and precision of AI algorithms and ultrasound for rotator cuff pathology detection.ResultsA total of eight studies met the inclusion criteria and were analysed. The review shows that AI techniques have shown promising results in diagnosing rotator cuff injuries, with reported comparative accuracy, specificity and sensitivity.ConclusionThe limited number of direct comparative studies makes it challenging to draw a definitive conclusion regarding superiority of one modality over another. However, the integration of AI in medical imaging holds significant potential to enhance diagnostic capabilities and streamline patient care.

Improving Tuberculosis Diagnosis using Explainable Artificial Intelligence in Medical Imaging

The integration of artificial intelligence (AI) applications in the healthcare sector is ushering in a &#x0D; significant transformation, particularly in developing more effective strategies for early diagnosis and treatment &#x0D; of contagious diseases like tuberculosis. Tuberculosis, a global public health challenge, demands swift &#x0D; interventions to prevent its spread. While deep learning and image processing techniques show potential in &#x0D; extracting meaningful insights from complex radiological images, their accuracy is often scrutinized due to a lack &#x0D; of explainability.&#x0D; This research navigates the intersection of AI and tuberculosis diagnosis by focusing on explainable &#x0D; artificial intelligence (XAI). A meticulously designed deep learning model for tuberculosis detection is introduced &#x0D; alongside an exploration of XAI to unravel complex decisions.&#x0D; The core belief is that XAI, by elucidating diagnostic decision rationale, enhances the reliability of AI &#x0D; in clinical settings. Emphasizing the pivotal role of XAI in tuberculosis diagnosis, this study aims to impact future &#x0D; research and practical implementations, fostering the adoption of AI-driven disease diagnosis methodologies for &#x0D; global health improvement.

Chapter 9. Return to the revision of the bioethics law regarding the use of artificial intelligence in the field of medical imaging.

On the occasion of the sixth AI international Summit (November 22-24, 2023), Professor Daniel Rueckert (Technical University of Munich) replies to our questions on ethical issues raised by the use of artificial intelligence in medical imaging.

Prostate cancer grading framework based on deep transfer learning and Aquila optimizer

Prostate cancer is the one of the most dominant cancer among males. It represents one of the leading cancer death causes worldwide. Due to the current evolution of artificial intelligence in medical imaging, deep learning has been successfully applied in diseases diagnosis. However, most of the recent studies in prostate cancer classification suffers from either low accuracy or lack of data. Therefore, the present work introduces a hybrid framework for early and accurate classification and segmentation of prostate cancer using deep learning. The proposed framework consists of two stages, namely classification stage and segmentation stage. In the classification stage, 8 pretrained convolutional neural networks were fine-tuned using Aquila optimizer and used to classify patients of prostate cancer from normal ones. If the patient is diagnosed with prostate cancer, segmenting the cancerous spot from the overall image using U-Net can help in accurate diagnosis, and here comes the importance of the segmentation stage. The proposed framework is trained on 3 different datasets in order to generalize the framework. The best reported classification accuracies of the proposed framework are 88.91% using MobileNet for the “ISUP Grade-wise Prostate Cancer” dataset and 100% using MobileNet and ResNet152 for the “Transverse Plane Prostate Dataset” dataset with precisions 89.22% and 100%, respectively. U-Net model gives an average segmentation accuracy and AUC of 98.46% and 0.9778, respectively, using the “PANDA: Resized Train Data (512 × 512)” dataset. The results give an indicator of the acceptable performance of the proposed framework.

Medical image fusion based on machine learning for health diagnosis and monitoring of colorectal cancer

With the rapid development of medical imaging technology and computer technology, the medical imaging artificial intelligence of computer-aided diagnosis based on machine learning has become an important part of modern medical diagnosis. With the application of medical image security technology, people realize that the difficulty of its development is the inherent defect of advanced image processing technology. This paper introduces the background of colorectal cancer diagnosis and monitoring, and then carries out academic research on the medical imaging artificial intelligence of colorectal cancer diagnosis and monitoring and machine learning, and finally summarizes it with the advanced computational intelligence system for the application of safe medical imaging.In the experimental part, this paper wants to carry out the staging preparation stage. It was concluded that the staging preparation stage of group Y was higher than that of group X and the difference was statistically significant. Then the overall accuracy rate of multimodal medical image fusion was 69.5% through pathological staging comparison. Finally, the diagnostic rate, the number of patients with effective treatment and satisfaction were analyzed. Finally, the average diagnostic rate of the new diagnosis method was 8.75% higher than that of the traditional diagnosis method. With the development of computer science and technology, the application field was expanding constantly. Computer aided diagnosis technology combining computer and medical images has become a research hotspot.

Advancements in cardiac structures segmentation: a comprehensive systematic review of deep learning in CT imaging.

Segmentation of cardiac structures is an important step in evaluation of the heart on imaging. There has been growing interest in how artificial intelligence (AI) methods-particularly deep learning (DL)-can be used to automate this process. Existing AI approaches to cardiac segmentation have mostly focused on cardiac MRI. This systematic review aimed to appraise the performance and quality of supervised DL tools for the segmentation of cardiac structures on CT. Embase and Medline databases were searched to identify related studies from January 1, 2013 to December 4, 2023. Original research studies published in peer-reviewed journals after January 1, 2013 were eligible for inclusion if they presented supervised DL-based tools for the segmentation of cardiac structures and non-coronary great vessels on CT. The data extracted from eligible studies included information about cardiac structure(s) being segmented, study location, DL architectures and reported performance metrics such as the Dice similarity coefficient (DSC). The quality of the included studies was assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). 18 studies published after 2020 were included. The DSC scores median achieved for the most commonly segmented structures were left atrium (0.88, IQR 0.83-0.91), left ventricle (0.91, IQR 0.89-0.94), left ventricle myocardium (0.83, IQR 0.82-0.92), right atrium (0.88, IQR 0.83-0.90), right ventricle (0.91, IQR 0.85-0.92), and pulmonary artery (0.92, IQR 0.87-0.93). Compliance of studies with CLAIM was variable. In particular, only 58% of studies showed compliance with dataset description criteria and most of the studies did not test or validate their models on external data (81%). Supervised DL has been applied to the segmentation of various cardiac structures on CT. Most showed similar performance as measured by DSC values. Existing studies have been limited by the size and nature of the training datasets, inconsistent descriptions of ground truth annotations and lack of testing in external data or clinical settings. [www.crd.york.ac.uk/prospero/], PROSPERO [CRD42023431113].

Radiologists' and Radiographers' Perspectives on Artificial Intelligence in Medical Imaging in Saudi Arabia.

Artificial intelligence (AI) in medical imaging rapidly expands regarding image processing and interpretation. Therefore, the aim was to explore radiographers' and radiologists' perceptions and attitudes towards AI use in medical imaging technologies in Saudi Arabia. The survey was distributed online, and responses were collected from 173 participants nationwide. Data analysis was performed using SPSS Statistics (version 27). The participants scored an average of 1.7, 1.6, and 1.8 on a scale of 1-3 for attitudinal perspectives on clinical application and the positive and negative impact of integrating AI technology in diagnostic radiology. Lack of knowledge (43.9%) and perceived cyber threats (37.7%) were the most cited factors hindering AI implementation in Saudi Arabia. The radiographradiology radiologists in this study had a favorable attitude toward AI integration in diagnostic radiology; nonetheless, concerns were raised about data protection, cyber security, AI-related errors, and decision-making challenges.

AI for Detection of Tuberculosis: Implications for Global Health.

Tuberculosis, which primarily affects developing countries, remains a significant global health concern. Since the 2010s, the role of chest radiography has expanded in tuberculosis triage and screening beyond its traditional complementary role in the diagnosis of tuberculosis. Computer-aided diagnosis (CAD) systems for tuberculosis detection on chest radiographs have recently made substantial progress in diagnostic performance, thanks to deep learning technologies. The current performance of CAD systems for tuberculosis has approximated that of human experts, presenting a potential solution to the shortage of human readers to interpret chest radiographs in low- or middle-income, high-tuberculosis-burden countries. This article provides a critical appraisal of developmental process reporting in extant CAD software for tuberculosis, based on the Checklist for Artificial Intelligence in Medical Imaging. It also explores several considerations to scale up CAD solutions, encompassing manufacturer-independent CAD validation, economic and political aspects, and ethical concerns, as well as the potential for broadening radiography-based diagnosis to other nontuberculosis diseases. Collectively, CAD for tuberculosis will emerge as a representative deep learning application, catalyzing advances in global health and health equity. Keywords: Computer-aided Diagnosis (CAD), Conventional Radiography, Thorax, Lung, Machine Learning Supplemental material is available for this article. © RSNA, 2024.