Detection Of Calcification Research Articles

MODERN POSSIBILITIES OF USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN CARDIOVASCULAR IMAGING

Cardiovascular diseases (CVD) are the leading cause of morbidity, disability and mortality worldwide. The emergence of new technologies and the introduction of artificial intelligence (AI) and machine learning (ML) have opened up opportunities for doctors to improve the effectiveness of diagnostic and therapeutic measures. The exponential development of AI, mainly in the fields of MO and deep learning (DL), is rapidly attracting the interest of clinicians in creating new integrated, reliable and effective methods of medical care. Cardiologists use a wide range of imaging-based diagnostic measures, which gives them access to more extensive quantitative information about patients compared to many other specialties. The purpose of the review is to summarize current literature data on the use of AI in the diagnosis of CVD, as well as to identify knowledge gaps that require further research. Cardiology is one of the fields of medicine where the methods of ML and DL have become widespread and have shown promising results. In echo-CG, SNN were successfully used to measure parameters of cardiac function. In cardiac CT, DL algorithms contributed to more accurate detection of coronary artery stenosis and calcification (CCA), and determination of plaque characteristics. In MRI, CNTs were used to solve problems such as automatic segmentation of chambers and structures of the heart, determination of tissue properties and perfusion analysis. As AI and MO technologies evolve, their integration opens up new opportunities. AI technologies are of great interest in the healthcare sector, due to the ability to analyze vast amounts of information in a short time, demonstrating high efficiency. AI can be an additional help to specialists, contributing to an increase in the efficiency of the workflow and medical care.

A novel deep learning-based pipeline architecture for pulp stone detection on panoramic radiographs.

Pulp stones are ectopic calcifications located in pulp tissue. The aim of this study is to introduce a novel method for detecting pulp stones on panoramic radiography images using a deep learning-based two-stage pipeline architecture. The first stage involved tooth localization with the YOLOv8 model, followed by pulp stone classification using ResNeXt. 375 panoramic images were included in this study, and a comprehensive set of evaluation metrics, including precision, recall, false-negative rate, false-positive rate, accuracy, and F1 score was employed to rigorously assess the performance of the proposed architecture. Despite the limited annotated training data, the proposed method achieved impressive results: an accuracy of 95.4%, precision of 97.1%, recall of 96.1%, false-negative rate of 3.9%, false-positive rate of 6.1%, and a F1 score of 96.6%, outperforming existing approaches in pulp stone detection. Unlike current studies, this approach adopted a more realistic scenario by utilizing a small dataset with few annotated samples, acknowledging the time-consuming and error-prone nature of expert labeling. The proposed system is particularly beneficial for dental students and newly graduated dentists who lack sufficient clinical experience, as it aids in the automatic detection of pulpal calcifications. To the best of our knowledge, this is the first study in the literature that propose a pipeline architecture to address the PS detection tasks on panoramic images.

Advanced Imaging Techniques for Atherosclerosis and Cardiovascular Calcification in Animal Models.

The detection and assessment of atherosclerosis and cardiovascular calcification can inform risk stratification and therapies to reduce cardiovascular morbidity and mortality. In this review, we provide an overview of current and emerging imaging techniques for assessing atherosclerosis and cardiovascular calcification in animal models. Traditional imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), offer non-invasive approaches of visualizing atherosclerotic calcification in vivo; integration of these techniques with positron emission tomography (PET) imaging adds molecular imaging capabilities, such as detection of metabolically active microcalcifications with 18F-sodium fluoride. Photoacoustic imaging provides high contrast that enables in vivo evaluation of plaque composition, yet this method is limited by optical penetration depth. Light-sheet fluorescence microscopy provides high-resolution, three-dimensional imaging of cardiovascular structures and has been used for ex vivo assessment of atherosclerotic calcification, but its limited tissue penetration and requisite complex sample preparation preclude its use in vivo to evaluate cardiac tissue. Overall, with these evolving imaging tools, our understanding of cardiovascular calcification development in animal models is improving, and the combination of traditional imaging techniques with emerging molecular imaging modalities will enhance our ability to investigate therapeutic strategies for atherosclerotic calcification.

Enhancing Mammography Images with Artificial Intelligence to Improve Radiological Diagnosis in Breast Cancer

Breast cancer is one of the most common types of cancer in women, and early diagnosis is life-saving. The aim of this study is to enhance the resolution of mammography images, thereby improving the contrast resolution, spatial resolution, and the detectability of calcifications, distortions, and opacities in the images. For this purpose, mammography images obtained from the open-access mini-MIAS dataset were used. Both the original dataset and the images processed with the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm underwent resolution enhancement using the Stable Diffusion artificial intelligence system. The results were evaluated by an expert radiologist, and it was determined that the diagnostic quality of the images significantly increased. These improvements aim to support early diagnosis in breast cancer and enhance diagnostic accuracy. Additionally, the applicability and effectiveness of these methods were emphasized, and the potential benefits of resolution enhancement techniques in clinical practice were discussed. The results have the potential to allow for more detailed and accurate analysis of mammography images, thereby improving patient care and treatment planning.

Evaluation of the flying focal spot technology in a wide-angle digital breast tomosynthesis system.

We characterize the flying focal spot (FFS) technology in digital breast tomosynthesis (DBT), designed to overcome source motion blurring. A wide-angle DBT system with continuous gantry and focus motion ("uncompensated focus") and a system with FFS were compared for image sharpness and lesion detectability. The modulation transfer function (MTF) was assessed as a function of height in the projections and reconstructed images, along with lesion detectability using the contrast detail phantom for mammography (CDMAM) and the L1 phantom. For the uncompensated focus system, the spatial frequency for 25% MTF value ( ) measured at 2, 4, and 6cm in DBT projections fell by 35%, 49%, and 59%, respectively in the tube-travel direction compared with the FFS system. There was no significant difference in for the front-back and tube-travel directions for the FFS unit. The in-plane MTF in the tube-travel direction also improved with the FFS technology.The threshold gold thickness ( ) for the 0.16-mm diameter discs of contrast detail phantom for mammography (CDMAM) improved for the FFS system in DBT mode, especially at greater heights above the table; at 45 and 65mm improved by 16% and 24%, respectively, compared with the uncompensated focus system. In addition, improvements in calcification and mass detection in a structured background were observed for DBT and synthetic mammography. The FFS system demonstrated faster scan times (4.8s versus 21.7s), potentially reducing patient motion artifacts. The FFS technology offers isotropic resolution, improved small detail detectability, and faster scan times in DBT mode compared with the traditional continuous gantry and focus motion approach.

Deep learning and radiomics-based vascular calcification characterization in dental cone beam computed tomography as a predictive tool for cardiovascular disease: A proof-of-concept study.

This study evaluated an automated deep learning method for detecting calcifications in the extracranial and intracranial carotid arteries and vertebral arteries in cone beam computed tomography (CBCT) scans. Additionally, a model utilizing CBCT-derived radiomics imaging biomarkers was evaluated to predict the cardiovascular diseases (CVD) of stroke and heart attack. Models were trained using the nn-UNet architecture to identify three locations of arterial calcifications: extracranial carotid calcification (ECC), intracranial carotid calcification (ICC), and vertebral artery calcification (VAC). In total, 148 scans were used for model training and validation. Radiomics features extracted from 135 calcification regions were used to characterize arterial calcifications for predicting CVD. The models demonstrated acceptable performance for detecting regions of calcification for ECC and ICC with bounding box accuracies of 0.71 ± 0.06 and 0.78 ± 0.12 respectively, although VAC performance was lower at 0.53 ± 0.17. Combining clinical data with radiomics for ICC improved stroke predictions, yielding an area under the curve derived from receiver operating characteristic analysis (AUC-ROC) of 0.94 ± 0.09, and combining data for ECC and ICC improved heart attack predictions, with AUC-ROC values of 0.88 ± 0.04 and 0.84 ± 0.16, respectively. Automated, quantifiable methods have potential for detecting ECC and ICC and predicting the incidence of cardiovascular disease based on arterial calcification detection in dental CBCT scans.

Diagnostic Accuracy of Calcified Aortic Knob Found in Chest Radiograph for Detection of Coronary Artery Calcification

Aims: To validate the diagnostic performance of a calcified aortic knob found in chest radiographs in relation to coronary calcification. Study Design: This study is an observational analytical cross-sectional study. Place and Duration of Study: This study examined participants who were referred for a CT coronary calcium scoring (CT-CAC). at the Central Chest Institute of Thailand, Department of Medical Services, Ministry of Public Health, during the period from November 1, 2019, to October 31, 2021. Methodology: This cross-sectional study aims to evaluate the association between calcified aortic knobs on chest radiographs and coronary artery calcification as determined by CT-CAC. The study included 664 patients who underwent CT-CAC between November 1, 2019 and October 31, 2021. We selected participants aged 40 to 75 years without known history of coronary artery disease (CAD) or diabetes (fasting blood sugar levels under 126 mg/dl within past 6 months). A total of 441 eligible patients were included in the final analysis. Standard chest radiographs within 6 months interval were evaluated and classified into 4 grades, Grade 0 (no visible calcification), Grade 1 (<50% calcification of aortic knob), Grade 2 (>50% calcification of the aortic knob), and Grade 3 (circular calcification of the aortic knob). These findings were then compared with CAC obtained via dual source CT scanner to evaluate the diagnostic accuracy of chest radiographs. Key performance metrics, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were calculated to determine the effectiveness of aortic knob calcification as a predictor for coronary calcification. Results: Positive CAC (CAC >0) was strongly associated with a calcified aortic knob (grades 1-3), with a high positive predictive value of 88.07%. The diagnostic accuracy, sensitivity, and specificity were 66.9%, 61.5%, and 79.8%, respectively. The negative predictive value was low, at 41.8%. Conclusion: A calcified aortic knob (grades 1-3) correlates strongly with positive CAC (positive predictive value 88.07%). The study’s findings of low sensitivity (61.5%) and negative predictive value (46.18%) indicate limitations in using calcified aortic knob on chest radiograph as a standalone screening tool for coronary artery calcification.

Influence of clinical experience in detecting calcifications of the head and neck region on panoramic radiographs: an app-based evaluation.

Digital panoramic radiographs (DPRs) are used in dental practice as the first diagnostic tool for the initial detection of head and neck regions soft-tissue calcifications. The aim of this study was to use a self-developed application (App) to evaluate the ability of dental students at different levels of training to examine known DPRs with different soft-tissue calcification. A total of known 100 DPRs with (n=50) and without (n=50) calcification were independently evaluated by four groups: preclinical, first clinical and last clinical dental students, and dentists with less than or equal to 1 year of professional experience in the same time (15min) and examination conditions. Unity software was used to develop the examination App, which allowed to mark areas with calcifications on the DPRs. The data were statistically analyzed between the groups (significance level: P<0.05) for every location, and the detection rate was calculated as a percentage of detected calcifications. Results revealed that the overall detection rate of calcifications in all groups was 29.17%. Dentists exhibited the highest detection rate (36.46%), followed by the last- (29.69%), first- (32.29%), and preclinical (15.10%) students. These findings suggest that clinical experience plays a role in the correct detection of soft-tissue calcifications in DPRs. However, deficiencies in radiological training during dental education may contribute to diagnostic errors. As these can become life-threatening risks, the results highlight the need for early training in the dental curriculum to improve diagnostic performance and minimize possible diagnostic errors.

Artificial Intelligence-based Software for Breast Arterial Calcification Detection on Mammograms.

The performance of a commercially available artificial intelligence (AI)-based software that detects breast arterial calcifications (BACs) on mammograms is presented. This retrospective study was exempt from IRB approval and adhered to the HIPAA regulations. Breast arterial calcification detection using AI was assessed in 253 patients who underwent 314 digital mammography (DM) examinations and 143 patients who underwent 277 digital breast tomosynthesis (DBT) examinations between October 2004 and September 2022. Artificial intelligence performance for binary BAC detection was compared with ground truth (GT) determined by the majority consensus of breast imaging radiologists. Area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value (NPV), accuracy, and BAC prevalence rates of the AI algorithm were compared. The case-level AUCs of AI were 0.96 (0.93-0.98) for DM and 0.95 (0.92-0.98) for DBT. Sensitivity, specificity, and accuracy were 87% (79%-93%), 92% (88%-96%), and 91% (87%-94%) for DM and 88% (80%-94%), 90% (84%-94%), and 89% (85%-92%) for DBT. Positive predictive value and NPV were 82% (72%-89%) and 95% (92%-97%) for DM and 84% (76%-90%) and 92% (88%-96%) for DBT, respectively. Results are 95% confidence intervals. Breast arterial calcification prevalence was similar for both AI and GT assessments. Breast AI software for detection of BAC presence on mammograms showed promising performance for both DM and DBT examinations. Artificial intelligence has potential to aid radiologists in detection and reporting of BAC on mammograms, which is a known cardiovascular risk marker specific to women.

Novel deep learning algorithm demonstrates strong performance for detection and quantification of breast arterial calcification and associates with cardiovascular disease

Abstract Background Breast arterial calcification (BAC) on mammography is associated with cardiovascular disease (CVD).1, 2 Given mammography is widely used as part of national breast cancer screening programs, routine reporting of BAC could address current limitations of CVD detection in women. Fully automated artificial intelligence (AI) software can facilitate seamless integration of BAC assessment alongside standard radiological evaluation of mammograms. and advancement in AI imaging software may result in clinically significant, superior BAC detection. Purpose To evaluate the performance of a deep-learning, fully-automated BAC detection algorithm against gold standard human BAC detection, and to evaluate its association with cardiovascular risk and disease. Methods A cohort of 285 women (mean age 61) with 2D digital mammography and CVD assessment with either an anatomic or functional test were evaluated. Cardiovascular risk factors were obtained from medical records. Visual BAC (vBAC) was assessed as present/absent by independent reviewers and BAC was scaled by an algorithm incorporating size, length, density and location (aBAC, 0-100). Diagnostic performance of vBAC and aBAC were compared and expressed as area under the receiver operator characteristic curve (AUC). Cardiovascular risk factor and disease prevalence was compared between vBAC and aBAC with CVD status defined by either abnormal anatomical or functional testing. Results The prevalence of vBAC and aBAC were 30.5% and 59.6%, respectively. The algorithm showed excellent discrimination for BAC detection compared with vBAC (AUC 0.92 [95% CI 0.89-0.98]), with sensitivity 94% and negative predictive value 96%. Interpretation took on average 144 seconds/patient with vBAC and 10 seconds/patient with aBAC (p&amp;lt;0.001). We identified 88 discordant cases (vBAC negative, aBAC positive), which were all represented by ‘faint’ BAC scores (&amp;lt;25). aBAC detected a greater proportion of patients with traditional cardiac risk factors, particularly in women &amp;lt;65 years (Figure 1). CVD was present in 136 (48%), among whom 37.5% were detected by vBAC and 61.7% by aBAC. CVD was present in 42% of the 88 discordant cases. Increasing BAC score was associated with increased relative risk of CVD presence (Figure 2). Conclusion Algorithmic scoring of BAC demonstrates a high performance and is faster to perform when compared to vBAC. Furthermore, it identifies greater proportion of women with higher cardiovascular risk and those with prevalent CVD. Lastly, aBAC detects ‘faint’ (visually missed) lesions that may have significant clinical implications.

Incidental detection and quantification of coronary artery calcification in ungated thoracic computed tomography for prognostic assessment. Preliminary results from the ICARUS registry

Abstract Background Coronary artery calcium (CAC) score improves cardiovascular risk stratification in asymptomatic individuals. However, whether CAC can be quantified in ungated thoracic computed tomography (uTCT) performed for other indications, and if this CAC score associates with cardiovascular outcomes, hasn’t been confirmed yet. Purpose We aim to analyse the extension of CAC and its prognostic value in terms of major adverse cardiovascular events (MACE) in patients who underwent uTCT for other indications. Methods We analysed a preliminary sample of 115 uTCT performed in 2015 in our hospital. We studied the presence and extension of CAC and quantified the global CAC score by Agatston method using validated software. Patients were categorized according to Coronary Artery Calcium Data and Reporting System (CAC-DRS) categories. Cardiovascular risk factors, lipidic control and pharmacological therapy of the cohort were registered. We analysed the time to first 3P-MACE, defined as cardiovascular death, non-fatal myocardial infarction and non-fatal stroke, whichever occurred first. A p-value &amp;lt;0.05 was considered statistically significant. Results Mean age of the cohort was 60.8±16.1 years (53% male, 53% smokers). Mean LDL-cholesterol was 121.67±34.91 mg/dL, and 35.1% received lipid-lowering therapy. In more than half (n=59, 51.3%) of the population CAC was detected. Mean CAC score was 312.99±774.48 Agatston units, and patients were categorized in CAC-DRS category 1 (n=19, 16.5%), category 2 (n=15, 13%) and category 3 (n=25, 21.7%). Patients with CAC were older (71±10.1 vs. 50.1±14.2 years, p&amp;lt;0.001), had higher burden of cardiovascular risk factors and prevalence of chronic kidney disease (CKD; n=8, 13.6% vs. n=1, 1.8%, p=0.03) and higher levels of triglycerides (143.2±80.5 vs. 106.8±36.2 mg/dL, p=0.004), but not LDL-cholesterol. Eleven (9.6%) patients presented 3P-MACE during a mean follow-up of 6.55±2.82 years. CAC score independently predicted 3P-MACE (HR 1.06 [1.01-1.11] per 100 Agatston units, p=0.02) along with CKD (HR 6.94 [1.58-30.46], p=0.01) and basal glucose levels (HR 1.03 [1.02-1.04] per mg/dL, p&amp;lt;0.001). Conclusions CAC can be incidentally detected in more than half of our unselected cohort of patients undergoing uTCT for other indications. CAC score quantification by Agatston method is feasible in uTCT and associates with a higher risk of MACE during follow-up.Incidental CAC detection in uTCT

Automated differentiation of calcifications and their clusters on the mammography image: the outcomes of the computer aided diagnosis system module

INTRODUCTION: Previously we developed the computer aided detection system (CAD) for mammography MammCheck II that increased the detection rate of small and difficult to detect breast carcinomas (BC). However this system was not specifically designed for calcification detection and discrimination. On the other hand, the calcifications had no influence on the CAD capability to detect BCs that appeared as a focal lesions.OBJECTIVE: To develop the approach for automated differentiation of benign and suspicious calcifications on the mammography images and assess its clinical value.MATERIALS AND METHODS: For the developed software testing we used a set of 390 mammography images with calcifications of all possible types (278 images with benign and 112 images with suspicious calcifications). For classification we used linear support vector machine (SVM) model, that was trained on the set of 126 images (70 — benign and 56 — suspicious). We developed two SVM models: with no vascular calcification analysis and with it. Statistics: for comparison between the normally distributed samples we used the Student’s T-test, for non-normally distributed — Wilcoxon signed-rank or Chi-square tests. For correlation testing of normally distributed samples the Paerson coefficient was calculated, for non-normally distributed samples — the Spearman or Kendall correlation coefficients. The statistical significance corresponded to Р-values &lt;0,05.RESULTS: During the testing of the first model version with no vascular calcification analysis we discovered the similarity of small early vascular calcifications and the suspicious ones. As a result this model falsely classified 14 of 23 (60.87%) vascular calcification clusters as suspicious. Therefore the model was improved. The final discrimination results for all calcification types (both benign and suspicious) obtained with the help of improved model were the following: true positive conclusions — 375/390 (96.15%), false positive conclusions — 15/390 (3.84%). In both cases when suspicious calcifications were classified as benign the wrong results were een only on one mammography view. At the same time, on another view the suspicious calcifications were correctly classified.DISCUSSION: During the CAD development it seems important not only mark the suspicious areas but also suppress false positive markings corresponding to the obviously benign lesions. However it is important during this operation not to suppress the true positive markings. Therefore such systems are inevitably characterized by a certain shift to decreased prognostic value of suspicious markings at the expense of the highest possible prognostic value of benign markings. In our viewpoint, the developed approach meets this requirement. Moreover, its integration into the CAD allows to suppress the markings of soft tissue lesions associated with typical benign calcifications, appeared on the previous processing steps. This capability may decrease the false positive rate of the main CAD module.CONCLUSION: The developed approach to benign and suspicious calcification discrimination (version with vascular calcification analysis) on the mammography image provided the sensitivity — 98.21%, specificity — 95.32%, negative predictive value (benign marking) — 99.25%, positive predictive value (suspicious marking) — 89.43%.

Why Current Detection of Vascular Calcification Falls Short and How to Improve on It.

Vascular calcification is a common phenomenon in various vascular diseases, where its presence heralds increased occurrence of adverse disease events, which invariably lead to increased morbidity and mortality in patients. Although the impact of calcification has become apparent, adequate and early detection of the most damaging form of early microcalcification is still in its infancy, preventing reliable identification of locations that would benefit from intervention. In this review, we will provide an overview of the current state-of-the-art noninvasive calcification imaging and its persisting limitations. We discuss promising approaches that may address these limitations in the future. In this context particular attention will be paid to imaging modalities such as CT, PET, and ultrasonography and molecular and cellular mechanisms and agents involved in physiological bone formation.

Detection of Medial Vascular Calcification in Patients with CKD Using Cost-Effective Classifiers

Detection of Medial Vascular Calcification in Patients with CKD Using Cost-Effective Classifiers

Susceptibility-Weighted Imaging for Differentiating Intracranial Calcification and Hemosiderin: A Comparative Study with CT

Background: Magnetic resonance imaging (MRI) is an essential diagnostic modality for evaluating various tissue types, but distinguishing between substances such as calcification and hemosiderin remains challenging due to their similar appearances on conventional MRI sequences. Susceptibility-weighted imaging (SWI) improves tissue contrast by incorporating phase information, potentially enhancing the accuracy of differentiating these substances. This study aims to assess the effectiveness of SWI, specifically the filtered phase image, in comparison with computed tomography (CT), which is considered the gold standard for calcification detection. Methods: A retrospective analysis was conducted on 35 patients, aged 50-80 years, who presented with confirmed cases of intracranial calcification or hemosiderin deposits. MRI was performed on a 1.5-tesla Siemens Essenza Magnetom using SWI sequences, including both phase and magnitude images. CT scans were obtained using a GE Optima 660 scanner. The study focused on 17 cases of calcification identified by both SWI and CT, and 18 cases of hemosiderin deposits detected by SWI. Statistical analysis was performed using Pearson correlation coefficients to evaluate the relationship between MRI and CT measurements. Results: The study revealed a strong correlation between CT and SWI in detecting calcifications, with a significant agreement in the measurement of calcification size. However, while phase and magnitude images demonstrated a strong correlation with one another, the filtered phase image did not show a significant correlation with hemosiderin size, suggesting limitations in its application for detecting hemosiderin deposits. CT scans confirmed calcification with Hounsfield units exceeding 100, while hemosiderin deposits were more effectively identified through SWI, which provided enhanced contrast without the use of ionizing radiation. Conclusion: SWI, particularly the filtered phase image, is a reliable and sensitive tool for detecting intracranial calcification, offering high accuracy compared to CT. While SWI proves effective in distinguishing calcification, further research is needed to optimize its use for detecting hemosiderin deposits. This study underscores the non-ionizing advantages of SWI over CT, advocating for its potential as a safer and equally effective alternative in clinical imaging.

Breast cancer characterization using region-based convolutional neural network with screening and diagnostic mammogram

Background: Detection and classification of microcalcifications in breast tissues is crucial for early breast cancer diagnosis and long-term treatment. Objective: This paper aims to propose a robust model capable of detection and classification of breast cancer calcifications in digital mammogram images using Deep Convolutional Neural Networks (DCNN). Materials and methods: An expert breast radiologist annotated the 3,265 clinical mammogram images to create a comprehensive ground truth dataset comprising 2,500 annotations for malignant and benign calcifications. This dataset was utilized to train our model, a two-stage detection system incorporating a Region-based Convolutional Neural Network (RCNN) with AlexNet and support vector machines to enhance the system’s robustness. The proposed model was compared to the one-stage detection, utilizing YOLOv4 combined with the Cross-Stage Partial Darknet53 (CSPDarknet53) architecture. A separate dataset of 504 mammogram images was explicitly set aside for model testing. The efficacy of the proposed model was evaluated based on key performance metrics, including precision, recall, F1 score, and mean average precision (mAP). Results: The results showed that the proposed RCNN-2 model could automatically identify and categorize calcifications as malignant or benign, outperforming the YOLOv4 models. The RCNN-2’s overall effectiveness, as evaluated by precision, recall, F1 score, and mean average precision (mAP), achieved scores of 0.82, 0.85, 0.83, and 0.74, respectively. Conclusion: The proposed RCNN-2 model demonstrates very effective detection and classification of calcification in mammogram images, especially in high-dense breast images. The performance of the proposed model was compared to that of YOLOv4, and it can be concluded that the proposed RCNN model yields outstanding performance. The model can be a helpful tool for radiologists.

Automated MRI-based segmentation of intracranial arterial calcification by restricting feature complexity.

To develop an automated deep learning model for MRI-based segmentation and detection of intracranial arterial calcification. A novel deep learning model under the variational autoencoder framework was developed. A theoretically grounded dissimilarity loss was proposed to refine network features extracted from MRI and restrict their complexity, enabling the model to learn more generalizable MR features that enhance segmentation accuracy and robustness for detecting calcification on MRI. The proposed method was compared with nine baseline methods on a dataset of 113 subjects and showed superior performance (for segmentation, Dice similarity coefficient: 0.620, area under precision-recall curve [PR-AUC]: 0.660, 95% Hausdorff Distance: 0.848 mm, Average Symmetric Surface Distance: 0.692 mm; for slice-wise detection, F1 score: 0.823, recall: 0.764, precision: 0.892, PR-AUC: 0.853). For clinical needs, statistical tests confirmed agreement between the true calcification volumes and predicted values using the proposed approach. Various MR sequences, namely T1, time-of-flight, and SNAP, were assessed as inputs to the model, and SNAP provided unique and essential information pertaining to calcification structures. The proposed deep learning model with a dissimilarity loss to reduce feature complexity effectively improves MRI-based identification of intracranial arterial calcification. It could help establish a more comprehensive and powerful pipeline for vascular image analysis on MRI.

Enhancing Breast Cancer Detection through Advanced AI-Driven Ultrasound Technology: A Comprehensive Evaluation of Vis-BUS.

This study aims to enhance breast cancer detection accuracy through an AI-driven ultrasound tool, Vis-BUS, developed by Barreleye Inc., Seoul, South Korea. Vis-BUS incorporates Lesion Detection AI (LD-AI) and Lesion Analysis AI (LA-AI), along with a Cancer Probability Score (CPS), to differentiate between benign and malignant breast lesions. A retrospective analysis was conducted on 258 breast ultrasound examinations to evaluate Vis-BUS's performance. The primary methods included the application of LD-AI and LA-AI to b-mode ultrasound images and the generation of CPS for each lesion. Diagnostic accuracy was assessed using metrics such as the Area Under the Receiver Operating Characteristic curve (AUROC) and the Area Under the Precision-Recall curve (AUPRC). The study found that Vis-BUS achieved high diagnostic accuracy, with an AUROC of 0.964 and an AUPRC of 0.967, indicating its effectiveness in distinguishing between benign and malignant lesions. Logistic regression analysis identified that 'Fatty' lesion density had an extremely high odds ratio (OR) of 27.7781, suggesting potential convergence issues. The 'Unknown' density category had an OR of 0.3185, indicating a lower likelihood of correct classification. Medium and large lesion sizes were associated with lower likelihoods of correct classification, with ORs of 0.7891 and 0.8014, respectively. The presence of microcalcifications showed an OR of 1.360. Among Breast Imaging-Reporting and Data System categories, category C5 had a significantly higher OR of 10.173, reflecting a higher likelihood of correct classification. Vis-BUS significantly improves diagnostic precision and supports clinical decision-making in breast cancer screening. However, further refinement is needed in areas like lesion density characterization and calcification detection to optimize its performance.

Relevance of ultrasound detection and assessment of vascular calcifications in chronic kidney disease

Introduction: Chronic kidney disease (CKD) affects approximately 10% of the Spanish population and constitutes an independent cardiovascular risk factor. Vascular calcifications, especially in the abdominal aorta, are significantly associated with increased cardiovascular mortality in patients with CKD. This systematic review evaluates the evidence on the usefulness of ultrasound to detect and assess vascular calcifications in patients with CKD, and its relevance as a cardiovascular risk factor.Methods: A systematic search of multiple electronic databases was conducted until July 2024. Original studies published between 2000 and 2024 evaluating the use of ultrasound to detect vascular calcifications in adult patients with CKD at any stage were included. Reviews, letters, editorials, animal studies and those not published in English or Spanish were excluded. Methodological quality was assessed using the Newcastle-Ottawa scale for observational studies and the Cochrane tool for clinical trials.Results: Twenty-eight studies were included with a total of 50 to 3,000 participants per study. Most were observational studies, with 3 randomised clinical trials. Ultrasound showed good correlation with computed tomography (r=0.65-0.82) to quantify aortic and femoral calcifications. The presence of aortic calcifications was associated with increased risk of cardiovascular mortality (HR 1.8-3.2) in patients with advanced CKD and on haemodialysis. Iliac/femoral calcifications were associated with increased risk of cardiovascular events (RR 1.6-2.4) in patients with stage 3-5 CKD. Incorporation of ultrasound assessment of calcifications into CKD-specific risk algorithms significantly improved their predictive performance.Discussion: Ultrasonography emerges as a valuable tool for the detection of vascular calcifications due to its non-invasive nature, low cost and ability to perform repeated assessments. However, limitations such as heterogeneity in assessment methods, variable sample sizes and lack of long-term follow-up in some studies were identified. Further research is needed to standardise protocols and assess the long-term impact of interventions based on ultrasound detection of calcifications.Conclusions: Ultrasonography is an effective and accessible tool for detecting and assessing vascular calcifications in patients with CKD, providing relevant prognostic information for cardiovascular risk stratification. Its consideration as a first-line tool in nephrological practice is recommended. However, large-scale prospective studies are needed to definitively validate its prognostic value in different stages of CKD and to evaluate the efficacy of early interventions based on these findings.

Improved Detection of Small and Low-Density Plaques in Virtual Noncontrast Imaging-based Calcium Scoring at Photon-Counting Detector CT.

Purpose To investigate the impact of plaque size and density on virtual noncontrast (VNC)-based coronary artery calcium scoring (CACS) using photon-counting detector CT and to provide safety net reconstructions for improved detection of subtle plaques in patients whose VNC-based CACS would otherwise be erroneously zero when compared with true noncontrast (TNC)-based CACS. Materials and Methods In this prospective study, CACS was evaluated in a phantom containing calcifications with different diameters (5, 3, and 1 mm) and densities (800, 400, and 200 mg/cm3) and in participants who underwent TNC and contrast-enhanced cardiac photon-counting detector CT (July 2021-March 2022). VNC images were reconstructed at different virtual monoenergetic imaging (55-80 keV) and quantum iterative reconstruction (QIR) levels (QIR,1-4). TNC scans at 70 keV with QIR off served as the reference standard. In vitro CACS was analyzed using standard settings (3.0-mm sections, kernel Qr36, 130-HU threshold). Calcification detectability and CACS of small and low-density plaques were also evaluated using 1.0-mm sections, kernel Qr44, and 120- or 110-HU thresholds. Safety net reconstructions were defined based on background Agatston scores and evaluated in vivo in TNC plaques initially nondetectable using standard VNC reconstructions. Results The in vivo cohort included 63 participants (57.8 years ± 15.5 [SD]; 37 [59%] male, 26 [41%] female). Correlation and agreement between standard CACSVNC and CACSTNC were higher in large- and medium-sized and high- and medium-density than in low-density plaques (in vitro: intraclass correlation coefficient [ICC] ≥ 0.90; r > 0.9 vs ICC = 0.20-0.48; r = 0.5-0.6). Small plaques were not detectable using standard VNC reconstructions. Calcification detectability was highest using 1.0-mm sections, kernel Qr44, 120- and 110-HU thresholds, and QIR level of 2 or less VNC reconstructions. Compared with standard VNC, using safety net reconstructions (55 keV, QIR 2, 110-HU threshold) for in vivo subtle plaque detection led to higher detection (increased by 89% [50 of 56]) and improved correlation and agreement of CACSVNC with CACSTNC (in vivo: ICC = 0.51-0.61; r = 0.6). Conclusion Compared with TNC-based calcium scoring, VNC-based calcium scoring was limited for small and low-density plaques but improved using safety net reconstructions, which may be particularly useful in patients with low calcium scores who would otherwise be treated based on potentially false-negative results. Keywords: Coronary Artery Calcium CT, Photon-Counting Detector CT, Virtual Noncontrast, Plaque Size, Plaque Density Supplemental material is available for this article. © RSNA, 2024.