Image Interpretation Research Articles

An initial exploration of factors that may impact radiographer performance in reporting mammograms

ObjectivesIn the United Kingdom, radiographers with a qualification in image interpretation have interpreted mammograms since 1995. These radiographers work under the title of radiography advanced practitioners (RAP) or Consultant Radiographer. This study extends upon what has been very recently published by exploring further clinical, non-clinical and experiential factors that may impact the reporting performance of RAPs. MethodsFifteen RAPs interpreted an image test set of 60 2D mammograms of known truth using the Detected-X software platform. Unknown to the reader, twenty cases contained a malignancy. Sensitivity, specificity, lesion sensitivity, receiver operating characteristic (ROC) and jack-knife free response operating characteristic (AFROC) values were established for each RAP. Specific features that had significant impact on accuracy were identified using Student's-T and Mann Whitney tests. ResultsRAPs with more than 10 years' experience in image interpretation, compared to those with less than 10 years’ experience, demonstrated lower specificity (51.3% vs 84.8%, p = 0.0264), ROC (0.83 vs 0.91, p = 0.0264) and AFROC (0.75 vs 0.87, p = 0.0037) values. Further, higher sensitivity values of 90.7% were seen in those RAPs who had an eye test in the last year compared to those who had not, 82% (p = 0.021). Other changes are presented in the paper. ConclusionThese data reveal previously unidentified factors that impact the diagnostic efficacy of RAPs when interpreting mammographic images. Highlighting such findings will empower screening authorities to better examine ways of standardising performance and offer a baseline for performance benchmarks. Implications for practiceThis study for the first time performs an initial exploration of the factors that may be associated with RAP performance when interpreting screening mammograms.

Advanced Computer Vision Techniques for Accurate Measurement in Unmanned Mobile Robots

Abstract For years, researchers have been studying computer vision, i.e. the ability of artificial intelligence (AI) systems to perceive and interpret visual data like humans. This study is gaining increasing attention as researchers aim to develop tools that automate visual tasks and replicate human visual awareness. However, the interpretation of images is very complex due to the vast amount of multi-resolution information they contain, making the development of AI technologies for visual recognition particularly challenging. This article provides an overview of digital image processing, highlighting the main concepts and introducing key algorithms. These methods are designed to capture, process, and interpret digital images and enable the extraction of important data from real-world environments. We conduct rigorous image processing tests and compare AI-driven recognition systems with human analysis. The results show that computer vision technology significantly outperforms human observation in terms of accuracy and consistency. These results highlight the potential of computer vision to revolutionize various industries by automating complex visual tasks and offer promising future applications in areas such as healthcare, security, and manufacturing. The paper provides valuable insights into current advances in digital image processing and the role of AI in improving visual recognition capabilities, paving the way for further innovation in this area.

An Efficient and Robust 3D Medical Image Classification Approach Based on 3D CNN, Time‐Distributed 2D CNN‐BLSTM Models, and mRMR Feature Selection

ABSTRACTThe advent of 3D medical imaging has been a turning point in the diagnosis of various diseases, as voxel information from adjacent slices helps radiologists better understand complex anatomical relationships. However, the interpretation of medical images by radiologists with different levels of expertise can vary and is also time‐consuming. In the last decades, artificial intelligence‐based computer‐aided systems have provided fast and more reliable diagnostic insights with great potential for various clinical purposes. This paper proposes a significant deep learning based 3D medical image diagnosis method. The method classifies MedMNIST3D, which consists of six 3D biomedical datasets obtained from CT, MRA, and electron microscopy modalities. The proposed method concatenates 3D image features extracted from three independent networks, a 3D CNN, and two time‐distributed ResNet BLSTM structures. The ultimate discriminative features are selected via the minimum redundancy maximum relevance (mRMR) feature selection method. Those features are then classified by a neural network model. Experiments adhere to the rules of the official splits and evaluation metrics of the MedMNIST3D datasets. The results reveal that the proposed approach outperforms similar studies in terms of accuracy and AUC.

Enhancing Breast Cancer Detection through Progressive Convolutional Neural Networks with Adaptive Kernel Pooling

Breast cancer is one of the leading causes of mortality among women worldwide, making early detection a crucial component in reducing fatality rates. Traditional diagnostic methods often rely on manual interpretation of mammograms or histopathological images, which can be time-consuming and prone to human error. Recent advances in deep learning, particularly Convolutional Neural Networks (CNNs), have demonstrated significant potential in automating and enhancing breast cancer detection. Breast cancer detection remains a critical challenge in medical diagnostics, where early identification can significantly improve patient outcomes. Leveraging advanced deep learning techniques, this research proposes a novel approach by integrating Progressive Convolutional Neural Networks (PCNNs) with Adaptive Kernel Pooling (AKP) for enhanced image classification. The model architecture dynamically adjusts convolutional filter sizes across layers to capture both coarse and fine-grained features, while AKP adapts pooling operations based on spatial dimensions, optimizing feature retention. Through rigorous experimentation on a large breast cancer histopathological dataset, the proposed method achieved an impressive accuracy of 98%, demonstrating its potential in real-world clinical applications. The high accuracy indicates the model's robustness in differentiating between malignant and benign tissues. This approach not only enhances detection precision but also addresses the challenge of feature loss during pooling, a common issue in traditional Convolutional Neural Networks (CNNs). Additionally, this study contributes to the growing field of medical imaging by offering an adaptable and scalable architecture that could be employed across various medical diagnostics beyond breast cancer detection. The proposed PCNN-AKP framework sets a new benchmark for automated cancer detection models, providing a pathway for future innovations in the use of deep learning for healthcare diagnostics.

Prostate magnetic resonance imaging (MRI) in patients with hip implants-presetting a protocol using a phantom.

Metal structures are a source of artifacts that significantly complicate the interpretation of magnetic resonance imaging (MRI). The use of prostate MRI as a preliminary test in men with a suspicion on prostate cancer leads to an increased use of the test. The aim of this study was to solve a clinically significant problem: to ensure the reduction of artifacts from metal hip implants during prostate MRI. Another goal was to evaluate the impact of artifact reduction methods on quantitative measurements. The prostate gland (PG) phantom model was a cylinder filled with an aqueous solution of polyvinylpyrrolidone at the concentrations of 40%, 30%, and 20% [central zone (CZ), peripheral zone (PZ), and "lesion", respectively]. Phantom MRI study was conducted on Philips Ingenia 1.5T and Philips Ingenia 3T scanners. For 1.5 T, the reduction in the influence of artifacts inside region of interest (ROI) was observed, expressed in a decrease in the average apparent diffusion coefficient (ADC) (CZ, PZ, "lesion") for the manual artifact reduction (MAR) and ZOOM (title of software artifact reduction) techniques compared to the standard method. For 3T this effect was not detected. The same ADC results were obtained for Standard and MAR techniques, and increased ADC values for ZOOM. Despite the fact that the spread of ADC values on 3.0T scanners was minimal, there was a significant deviation of ADC values from the reference ones (up to 30.4%). Therefore, it is necessary to use a correction coefficient in the ADC calculation for the 3.0 T device. In the presented clinical case, high-quality tomograms were obtained without any artifacts, despite the presence of two hip replacement devices in the scanning area. The accurate prostate MRI in the presence of implants is essential for an accurate diagnosis. This approach allows to reduce artifacts from hip implants, to visualize PG and periprostatic tissue in the best way, and to detect malignant and benign changes.

Real-world evaluation of Brainomix e-Stroke software

Background and purposeBrainomix e-Stroke is an artificial intelligence-based decision support tool that aids the interpretation of CT imaging in the context of acute stroke. While e-Stroke has the potential to...

Comparative Accuracy of ChatGPT 4.0 and Google Gemini in Answering Pediatric Radiology Text-Based Questions.

This study evaluates the accuracy of two AI language models, ChatGPT 4.0 and Google Gemini (as of August 2024), in answering a set of 79 text-based pediatric radiology questions from "Pediatric Imaging: A Core Review."Accurate interpretation of text and images is critical in radiology, making AI tools valuable in medical education. The study involved 79 questions selected from a pediatric radiology question set, focusing solely on text-based questions. ChatGPT 4.0 and Google Gemini answered these questions, and their responses were evaluated using a binary scoring system. Statistical analyses, including chi-square tests and relative risk (RR) calculations, were performed to compare the overall and subsection accuracy of the models. ChatGPT 4.0 demonstrated superior accuracy, correctly answering 83.5% (66/79) of the questions, compared to Google Gemini's 68.4% (54/79), with a statistically significant difference (p=0.0255, RR=1.221). No statistically significant differences were found between the models within individual subsections, with p-values ranging from 0.136 to 1. ChatGPT 4.0 outperformed Google Gemini in overall accuracy for text-based pediatric radiology questions, highlighting its potential utility in medical education. However, the lack of significant differences within subsections and the exclusion of image-based questions underscore the need for further research with larger sample sizes and multimodal inputs to fully assess AI models' capabilities in radiology.

Optical coherence tomography to guide percutaneous coronary intervention.

Percutaneous coronary intervention (PCI) has been most commonly guided by coronary angiography. However, to overcome the inherent limitations of conventional coronary angiography, there has been an increasing interest in the adjunctive tools of intracoronary imaging for PCI guidance. Recently, optical coherence tomography (OCT) has garnered substantial attention as a valid intravascular imaging modality for guiding PCI. However, despite the unparalleled high-resolution imaging capability of OCT, which offers detailed anatomical information on coronary lesion morphology and PCI optimisation, its broad application in routine PCI practice remains limited. Several factors may have curtailed the widespread adoption of OCT-guided PCI in daily practice, including the transitional challenge from intravascular ultrasound (IVUS), the experienced skill required for image acquisition and interpretation, the lack of a uniform algorithm for OCT-guided PCI optimisation, and the limited clinical evidence. Herein, we provide an in-depth review of OCT-guided PCI, involving the technical aspects, optimal strategies for OCT-guided PCI, and the wide application of OCT-guided PCI in various anatomical subsets. Special attention is given to the latest clinical evidence from recent randomised clinical trials with respect to OCT-guided PCI.

Concordance Between the Expert Reading of Biparametric-MRI and the Nonexpert Multiparametric-MRI for the Detection of Clinically Significant Prostate Cancer: Clinical Implications

PurposeProstate-magnetic resonance imaging (MRI) interpretation is challenging, with expertise playing a crucial role. Biparametric MRI (bpMRI) is gaining popularity in experienced centers due to its time and cost advantages over multiparametric MRI (mpMRI). We aim to analyze concordance between nonexpert radiologist PI-RADS from mpMRI and expert radiologist PI-RADS from bpMRI, and its clinical implications. Material and Methods222 men suspected of having prostate cancer (PCa) and mpMRI reported by nonexpert radiologists were referred to a reference center for transperineal MRI-TRUS fusion biopsy where an expert radiologist reported bpMRI PI-RADS 2.1 and segmentation, blinded to external mpMRI. Mapping targeted suspected lesions and 12-core systematic biopsies were performed. Clinically significant PCa (csPCa) was diagnosed when ISUP-grade group was ≥2. ResultsConcordance between both PI-RADS existed in 49.1% of cases (Kappa index 0.288). In 102 cases (45.9%), expert reclassification to lower PI-RADS existed, while an increase existed in 11 cases (5.0%), P < .001. Agreement existed in 30.8% of nonexpert PI-RADS 3, 43.6% of PI-RADS 4, and 83.7% of PI-RADS 5, P < .001. Potential clinical implications included 27% reduction in prostate biopsies when using expert bpMRI readings compared to nonexpert mpMRI readings (P < 0.001), while undetected csPCa were 4.2% and 3.4%, respectively, P = .669. Over-detection reduction of insignificant PCa was 29.4% and 0%, respectively, P = .034. ConclusionsConcordance between nonexpert PI-RADS mpMRI and expert PI-RADS bpMRI was low, increasing with nonexpert PI-RADS. Expert reclassification would reduce prostate biopsies by more than one quarter and over-detection of iPCa, while csPCa detection remained similar.

Artificial Intelligence in Temporal Bone Imaging: A Systematic Review.

The human temporal bone comprises more than 30 identifiable anatomical components. With the demand for precise image interpretation in this complex region, the utilization of artificial intelligence (AI) applications is steadily increasing. This systematic review aims to highlight the current role of AI in temporal bone imaging. A Systematic Review of English Publications searching MEDLINE (PubMed), COCHRANE Library, and EMBASE. The search algorithm employed consisted of key items such as 'artificial intelligence,' 'machine learning,' 'deep learning,' 'neural network,' 'temporal bone,' and 'vestibular schwannoma.' Additionally, manual retrieval was conducted to capture any studies potentially missed in our initial search. All abstracts and full texts were screened based on our inclusion and exclusion criteria. A total of 72 studies were included. 95.8% were retrospective and 88.9% were based on internal databases. Approximately two-thirds involved an AI-to-human comparison. Computed tomography (CT) was the imaging modality in 54.2% of the studies, with vestibular schwannoma (VS) being the most frequent study item (37.5%). Fifty-eight out of 72 articles employed neural networks, with 72.2% using various types of convolutional neural network models. Quality assessment of the included publications yielded a mean score of 13.6 ± 2.5 on a 20-point scale based on the CONSORT-AI extension. Current research data highlight AI's potential in enhancing diagnostic accuracy with faster results and decreased performance errors compared to those of clinicians, thus improving patient care. However, the shortcomings of the existing research, often marked by heterogeneity and variable quality, underscore the need for more standardized methodological approaches to ensure the consistency and reliability of future data. NA Laryngoscope, 2024.

Automated Borehole Image Interpretation Using Computer Vision and Deep Learning

Automated Borehole Image Interpretation Using Computer Vision and Deep Learning

Nephrology Tools: Using Chatbots for Image Interpretation and Answering Questions

Nephrology Tools: Using Chatbots for Image Interpretation and Answering Questions

Evaluation of Hearing Loss: Understanding Audiologic Testing to Refine Image Interpretation.

The standard of reference for diagnosing and characterizing hearing loss is audiologic testing. The results of audiologic testing inform the imaging algorithm and the differential diagnosis for the underlying cause. Pure-tone audiometry tests the ability to hear tones across different frequencies, and the results are displayed as an audiogram. Tympanometry measures tympanic membrane compliance as a function of pressure to generate a tympanogram. Acoustic reflex testing helps differentiate third window lesions from other causes of conductive hearing loss. Clinical and audiologic assessment of sensorineural hearing loss helps in differentiating cochlear from retrocochlear causes. Symmetrical sensorineural hearing loss is typical of cochlear disease. Asymmetry increases the likelihood of a retrocochlear lesion, the most common of which among adults is vestibular schwannoma. Unlike patients with sensorineural hearing loss, who commonly have normal imaging studies, patients with conductive hearing loss are expected to have abnormal temporal bone CT studies. By incorporating the results of audiologic testing into their evaluation, radiologists can perform a more informed and more intentional search for the structural cause of hearing loss. The authors describe several audiogram configurations that suggest specific underlying mechanisms of conductive hearing loss. By providing a practical and accessible summary of the basics of audiologic testing, the authors empower the radiologist to leverage relevant clinical information and audiologic test results to interpret temporal bone imaging more confidently and more accurately, particularly temporal bone CT in the setting of conductive hearing loss. ©RSNA, 2024.

Influence of Prior Imaging Review on Recommendations for Additional Diagnostic Testing: Retrospective Analysis of Imaging Reports in a Chiropractic Radiology Practice

ObjectiveThe purpose of this study was to assess the influence of prior imaging review on recommendations for additional diagnostic testing in an academic chiropractic radiology practice. A secondary aim was to explore the influence of prior imaging review on radiographic interpretation. MethodsWe retrospectively reviewed radiology reports generated from July 18, 2022, to July 18, 2023, from the Palmer College of Chiropractic main campus (Davenport, Iowa) clinic system. Imaging interpretation included an automated search for prior images in an internal picture archival and communication system (PACS). Images from regional health system databases were available and sought by radiologists when (1) unclear radiologic findings had potential clinical implications or (2) prior imaging could clarify potential problems detected in a clinical history. Data were abstracted to a secure adaptive electronic questionnaire and analyzed descriptively. ResultsWe reviewed 1712 radiographic and 165 musculoskeletal diagnostic ultrasound reports for 1552 unique individuals (811 [52.3%] females and 741 [47.7%] males) with a mean age of 42.1 years (range, 2-93 years). Prior imaging was described in 417 (22.2%) reports; 246 (58.9%) indicated images from internal PACS, 192 (46.0%) indicated images from external PACS, and 21 noted both internal and external PACS. Prior imaging findings were credited with answering a clinical question in 98 (23.5%), and a radiographic question in 228 (54.7%) of 417 reports. The process negated the need for follow-up diagnostic testing recommendations in 119 (28.5%) instances, leading to additional imaging recommendations in 19 (4.6%). ConclusionData obtained in this study suggest that comparing current and previous imaging may help reduce unnecessary additional imaging or follow-up diagnostic testing recommendations. Prior imaging review may also facilitate diagnostic confidence and interpretation clarity.

Ocular Trauma: Anatomy, Pitfalls, and Systematic Approach to Imaging Interpretation.

Ocular Trauma: Anatomy, Pitfalls, and Systematic Approach to Imaging Interpretation.

Good Practices in using Instructional Images to Enhance Young Children's Linguistic Skills from the Viewpoint of Language Education Experts

The increasing emphasis on improving early childhood language education necessitates a deeper exploration of effective pedagogical tools, particularly visual aids. This study aims to examine the perspectives of language education specialists regarding best practices for employing images to enhance language development in early childhood settings. A targeted sample of 33 experts in language teaching, all working in early childhood education institutions in northern Jordan, was selected for the study. Qualitative research methodology was adopted, utilizing semi-structured interviews and grounded theory for data analysis. The findings identified a series of best practices for the use of instructional images to foster language development in young learners. Key practices included avoiding overly explicit interpretations of images by teachers, cultivating teachers’ confidence in children’s ability to interpret and engage with images independently, providing immediate feedback after children view images, allowing ample opportunities for children to articulate their interpretations of image content, ensuring clear and visible image presentation, selecting images that resonate with children’s real-life experiences, and accommodating individual differences among children. Based on these results, the study put forth several recommendations aimed at enhancing language acquisition through the strategic use of visual aids. The research highlights the critical role of creating a learning environment that effectively integrates images as a tool for promoting language skills in early childhood, encouraging a more interactive and engaging approach to language education.

HIPI: Spatially resolved multiplexed protein expression inferred from H&E WSIs.

Solid tumors are characterized by complex interactions between the tumor, the immune system and the microenvironment. These interactions and intra-tumor variations have both diagnostic and prognostic significance and implications. However, quantifying the underlying processes in patient samples requires expensive and complicated molecular experiments. In contrast, H&E staining is typically performed as part of the routine standard process, and is very cheap. Here we present HIPI (H&E Image Interpretation and Protein Expression Inference) for predicting cell marker expression from tumor H&E images. We process paired H&E and CyCIF images taken from serial sections of colorectal cancers to train our model. We show that our model accurately predicts the spatial distribution of several important cell markers, on both held-out tumor regions as well as new tumor samples taken from different patients. Moreover, using only the tissue image morphology, HIPI is able to colocalize the interactions between different cell types, further demonstrating its potential clinical significance.

Stage interpretation of psychological images in the dramaturgy of Eduardo de Filippo in the Uzbek theatre

Stage interpretation of psychological images in the dramaturgy of Eduardo de Filippo in the Uzbek theatre

Concordance and Discordance Between Radiology Residents and Consultant Radiologist Interpretation Of CT Brain

OBJECTIVES The primary objective of this study is to assess the degree of concordance and discordance between the interpretations of computed tomography (CT) brain images by resident and consultant radiologists while emphasizing the critical significance of accurate image interpretation for informed clinical decision-making. METHODOLOGY The evaluation of radiology reports for CT Brain interpretation through a prospective analysis at the Radiology Department of Rehman Medical Institute over two years, from 1st October 2020 to 31st October 2022. A total of 198 patients who underwent cranial CT scans were interpreted by residents (R1, R2, R3, R4). Following this, the consultant radiologists reviewed the images and completed their reports. The reports of the residents and the consultant radiologists were then compared, and concordance was achieved when the residents’ reports were consistent with the final radiologist’s reports. The data collected were recorded in Microsoft Excel. The statistical analysis was performed using SPSS version 22 (IBM Corp., Armonk, NY), and the kappa coefficient was used to determine the level of agreement between residents and consultants.RESULTSAmong the 198 CT Head reports evaluated, 186 of them were in agreement with the final report of the consultant radiologist. Of the correctly diagnosed cases, R1 correctly diagnosed 46 cases, R2 correctly diagnosed 80 cases, R3 correctly diagnosed 54 cases, and R4 correctly diagnosed 6 cases. Our study achieved a percentage agreement of 93.93, with a Cohen's kappa coefficient of 0.8.CONCLUSIONThe overall concordance rate between residents and consultant radiologists was 93.93%, with a kappa coefficient 0.8. This high kappa coefficient indicates strong agreement between the two groups.

The Accuracy of Radiographers Preliminary Image Evaluations of Axial Radiographs: a Prospective Longitudinal Study at Al-Hussein Hospital, Iraq

Introduction: Modern emergency departments are under increasing pressure that can only be alleviated by new approaches to meeting demand. One strategy could be the adoption of radiographer preliminary image evaluations (PIEs), which would significantly reduce the time it takes emergency teams to receive critical imagery results. The aim of this study was to examine the accuracy of radiographers’ interpretations of axial radiographs and the impact a short training program had on their image interpretation skills. Methods: Forty-one radiographers volunteered to participate in the study. From 15 January to 15 April 2023 radiographers produced PIEs for 24 axial radiographs which were compared to the radiology reports for the same images. The accuracy each of the radiographers’ PIEs were categorized as true positive, true negative, false positive and false negative, with numerical scores to enable mean accuracies to be calculated. The radiographers’ accuracy was then reassessed on the same images after they had completed a two-week training program on axial skeletal radiograph interpretation. Results: The study reported that demonstrated that most (TP) radiographer PIEs (M = 5.66, SD = 1.24) were about facial bone fracture. Moreover, this study demonstrated that most (FN/TP) radiographer PIEs (M = 3.83, SD = 1.69) were that cases about inflammatory disease of the axial spine, such as ankylosing spondylitis. In contrast, the most common cases misinterpreted were about cervical spine disorders such as diffuse idiopathic skeletal hyperostosis (DISH) (M = 2.49, SD = 1.36). Years of experience in radiography showed a positive but not statistically significant influence on accuracy. Additionally, post training mean accuracy significantly improved. Conclusion: This study found radiographers provided quite accurate PIEs for facial bone fractures when compared to the full radiology reports of the same images. The findings also highlighted specific pathologies that were frequently misinterpreted. A two-week training program was found to be effective at improving radiographers’ ability to correctly identify and describe abnormalities present in axial radiographs.