Music Therapy as a Complementary Treatment in Patients with Dementia Associated to Alzheimer's Disease: A Systematic Review.

Background: Alzheimer's disease (AD) is a complex condition that affects various aspects of a patient's life. Music therapy may be considered a beneficial supplementary tool to traditional therapies, that not fully address the range of AD manifestations. Objective: The purpose of this systematic review is to investigate whether music therapy can have a positive impact on AD patients and on which symptoms. Methods: The main research databases employed have been PubMed and Cochrane, using the keywords "dementia", "music therapy", "Alzheimer", "fMRI", "music", and "EEG". Results: After removing duplicates and irrelevant studies, 23 were screened using set criteria, resulting in the final inclusion of 15 studies. The total number of participants included in these studies has been of 1,196 patients. For the fMRI analysis the search resulted in 28 studies on PubMed, two of which were included in the research; the total number of participants was of 124 individuals. The studies conducted with EEG were found using PubMed. The initial search resulted in 15 studies, but after a more accurate evaluation only 2 have been included in the analysis. Conclusions: Even though the data currently available is not sufficient to draw conclusions supported by robust statistical power, the impact of music therapy on AD neuropsychiatric symptoms deserves great interest. Further research should be ushered, possibly multicentric studies, led with neuroimaging and other recent techniques, which can eventually open views on the music role in improving the cognitive status in AD.

Coronary Artery Calcium Score Predicts Major Adverse Cardiovascular Events in Stable Chest Pain.

Background Coronary artery calcium (CAC) has prognostic value for major adverse cardiovascular events (MACE) in asymptomatic individuals, whereas its role in symptomatic patients is less clear. Purpose To assess the prognostic value of CAC scoring for MACE in participants with stable chest pain initially referred for invasive coronary angiography (ICA). Materials and Methods This prespecified subgroup analysis from the Diagnostic Imaging Strategies for Patients With Stable Chest Pain and Intermediate Risk of Coronary Artery Disease (DISCHARGE) trial, conducted between October 2015 and April 2019 across 26 centers in 16 countries, focused on adult patients with stable chest pain referred for ICA. Participants were randomly assigned to undergo either ICA or coronary CT. CAC scores from noncontrast CT scans were categorized into low, intermediate, and high groups based on scores of 0, 1-399, and 400 or higher, respectively. The end point of the study was the occurrence of MACE (myocardial infarction, stroke, and cardiovascular death) over a median 3.5-year follow-up, analyzed using Cox proportional hazard regression tests. Results The study involved 1749 participants (mean age, 60 years ± 10 [SD]; 992 female). The prevalence of obstructive coronary artery disease (CAD) at CT angiography rose from 4.1% (95% CI: 2.8, 5.8) in the CAC score 0 group to 76.1% (95% CI: 70.3, 81.2) in the CAC score 400 or higher group. Revascularization rates increased from 1.7% to 46.2% across the same groups (P < .001). The CAC score 0 group had a lower MACE risk (0.5%; HR, 0.08 [95% CI: 0.02, 0.30]; P < .001), as did the 1-399 CAC score group (1.9%; HR, 0.27 [95% CI: 0.13, 0.59]; P = .001), compared with the 400 or higher CAC score group (6.8%). No significant difference in MACE between sexes was observed (P = .68). Conclusion In participants with stable chest pain initially referred for ICA, a CAC score of 0 showed very low risk of MACE, and higher CAC scores showed increasing risk of obstructive CAD, revascularization, and MACE at follow-up. Clinical trial registration no. NCT02400229 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Hanneman and Gulsin in this issue.

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

Quantitative medicine: Tracing the transition from holistic to reductionist approaches. A new "quantitative holism" is possible?

The practice of medicine has evolved significantly over time, from a more holistic to a reductionist or mechanistic approach. This paper briefly traces the history of medicine and the transition to quantitative medicine, which has enabled more personalized and targeted treatments, and improved understanding of the underlying biological mechanisms of disease. However, this shift has also presented some challenges and criticisms, including the danger of losing sight of the patient as a unique, whole individual. This paper explores the underlying principles and key contributions of quantitative medicine, as well as the context for its rise, including the development of new technologies and the influence of reductionist philosophies. The challenges and criticisms of this approach, and the need to balance reductionist and holistic approaches in order to achieve a comprehensive understanding of human health will be discussed. Ultimately, by integrating insights from philosophy, physics, and other fields, we may be able to develop new and innovative approaches that bridge the gap between reductionism and holism and improve patient outcomes with the new "quantitative holism."

Advances in Imaging for Assessing Pelvic Endometriosis.

In recent years, due to the development of standardized diagnostic protocols associated with an improvement in the associated technology, the diagnosis of pelvic endometriosis using imaging is becoming a reality. In particular, transvaginal ultrasound and magnetic resonance are today the two imaging techniques that can accurately identify the majority of the phenotypes of endometriosis. This review focuses not only on these most common imaging modalities but also on some additional radiological techniques that were proposed for rectosigmoid colon endometriosis, such as double-contrast barium enema, rectal endoscopic ultrasonography, multidetector computed tomography enema, computed tomography colonography and positron emission tomography-computed tomography with 16α-[18F]fluoro-17ß-estradiol.

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson's Disease Affected by COVID-19: A Narrative Review.

Background and Motivation: Parkinson's disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

Cardiovascular/Stroke Risk Stratification in Parkinson's Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review.

Parkinson's disease (PD) is a severe, incurable, and costly condition leading to heart failure. The link between PD and cardiovascular disease (CVD) is not available, leading to controversies and poor prognosis. Artificial Intelligence (AI) has already shown promise for CVD/stroke risk stratification. However, due to a lack of sample size, comorbidity, insufficient validation, clinical examination, and a lack of big data configuration, there have been no well-explained bias-free AI investigations to establish the CVD/Stroke risk stratification in the PD framework. The study has two objectives: (i) to establish a solid link between PD and CVD/stroke; and (ii) to use the AI paradigm to examine a well-defined CVD/stroke risk stratification in the PD framework. The PRISMA search strategy selected 223 studies for CVD/stroke risk, of which 54 and 44 studies were related to the link between PD-CVD, and PD-stroke, respectively, 59 studies for joint PD-CVD-Stroke framework, and 66 studies were only for the early PD diagnosis without CVD/stroke link. Sequential biological links were used for establishing the hypothesis. For AI design, PD risk factors as covariates along with CVD/stroke as the gold standard were used for predicting the CVD/stroke risk. The most fundamental cause of CVD/stroke damage due to PD is cardiac autonomic dysfunction due to neurodegeneration that leads to heart failure and its edema, and this validated our hypothesis. Finally, we present the novel AI solutions for CVD/stroke risk prediction in the PD framework. The study also recommends strategies for removing the bias in AI for CVD/stroke risk prediction using the PD framework.

Association between Hypomagnesemia, COVID-19, Respiratory Tract and Lung Disease.

The complexity of COVID-19 is also related to the multiple molecular pathways triggered by SARS-CoV-2, which is able to cause type I pneumocyte death, trigger intravascular coagulation, interfere with the renin-angiotensin system, dysregulate iron metabolism, ending with the insurgence of a cytokine storm which may lead to death. Old adults with obesity, hypertension, and diabetes are among the high-risk category groups more prone to SARS-CoV-2 infection. Magnesium has been reported to play a major role both in physiology and in pathology, particularly in elderly people, regulating cytotoxic functions of natural killer (NK) cells and CD8+ T lymphocytes. In spite of the absence of controlled trials, the possibility of magnesium supplementation for supportive treatment in patients with COVID-19 should be encouraged. This could be useful in all phases of the COVID-19 disease.

Zinc as a Drug for Wilson's Disease, Non-Alcoholic Liver Disease and COVID-19-Related Liver Injury.

Zinc is the second most abundant trace element in the human body, and it plays a fundamental role in human physiology, being an integral component of hundreds of enzymes and transcription factors. The discovery that zinc atoms may compete with copper for their absorption in the gastrointestinal tract let to introduce zinc in the therapy of Wilson's disease, a congenital disorder of copper metabolism characterized by a systemic copper storage. Nowadays, zinc salts are considered one of the best therapeutic approach in patients affected by Wilson's disease. On the basis of the similarities, at histological level, between Wilson's disease and non-alcoholic liver disease, zinc has been successfully introduced in the therapy of non-alcoholic liver disease, with positive effects both on insulin resistance and oxidative stress. Recently, zinc deficiency has been indicated as a possible factor responsible for the susceptibility of elderly patients to undergo infection by SARS-CoV-2, the coronavirus responsible for the COVID-19 pandemic. Here, we present the data correlating zinc deficiency with the insurgence and progression of Covid-19 with low zinc levels associated with severe disease states. Finally, the relevance of zinc supplementation in aged people at risk for SARS-CoV-2 is underlined, with the aim that the zinc-based drug, classically used in the treatment of copper overload, might be recorded as one of the tools reducing the mortality of COVID-19, particularly in elderly people.

Wilson disease tissue classification and characterization using seven artificial intelligence models embedded with 3D optimization paradigm on a weak training brain magnetic resonance imaging datasets: a supercomputer application.

Wilson's disease (WD) is caused by copper accumulation in the brain and liver, and if not treated early, can lead to severe disability and death. WD has shown white matter hyperintensity (WMH) in the brain magnetic resonance scans (MRI) scans, but the diagnosis is challenging due to (i) subtle intensity changes and (ii) weak training MRI when using artificial intelligence (AI). Design and validate seven types of high-performing AI-based computer-aided design (CADx) systems consisting of 3D optimized classification, and characterization of WD against controls. We propose a "conventional deep convolution neural network" (cDCNN) and an "improved DCNN" (iDCNN) where rectified linear unit (ReLU) activation function was modified ensuring "differentiable at zero." Three-dimensional optimization was achieved by recording accuracy while changing the CNN layers and augmentation by several folds. WD was characterized using (i) CNN-based feature map strength and (ii) Bispectrum strengths of pixels having higher probabilities of WD. We further computed the (a) area under the curve (AUC), (b) diagnostic odds ratio (DOR), (c) reliability, and (d) stability and (e) benchmarking. Optimal results were achieved using 9 layers of CNN, with 4-fold augmentation. iDCNN yields superior performance compared to cDCNN with accuracy and AUC of 98.28 ± 1.55, 0.99 (p < 0.0001), and 97.19 ± 2.53%, 0.984 (p < 0.0001), respectively. DOR of iDCNN outperformed cDCNN fourfold. iDCNN also outperformed (a) transfer learning-based "Inception V3" paradigm by 11.92% and (b) four types of "conventional machine learning-based systems": k-NN, decision tree, support vector machine, and random forest by 55.13%, 28.36%, 15.35%, and 14.11%, respectively. The AI-based systems can potentially be useful in the early WD diagnosis. Graphical Abstract.

Ultrasound-based internal carotid artery plaque characterization using deep learning paradigm on a supercomputer: a cardiovascular disease/stroke risk assessment system.

Visual or manual characterization and classification of atherosclerotic plaque lesions are tedious, error-prone, and time-consuming. The purpose of this study is to develop and design an automated carotid plaque characterization and classification system into binary classes, namely symptomatic and asymptomatic types via the deep learning (DL) framework implemented on a supercomputer. We hypothesize that on ultrasound images, symptomatic carotid plaques have (a) a low grayscale median because of a histologically large lipid core and relatively little collagen and calcium, and (b) a higher chaotic (heterogeneous) grayscale distribution due to the composition. The methodology consisted of building a DL model of Artificial Intelligence (called Atheromatic 2.0, AtheroPoint, CA, USA) that used a classic convolution neural network consisting of 13 layers and implemented on a supercomputer. The DL model used a cross-validation protocol for estimating the classification accuracy (ACC) and area-under-the-curve (AUC). A sample of 346 carotid ultrasound-based delineated plaques were used (196 symptomatic and 150 asymptomatic, mean age 69.9 ± 7.8 years, with 39% females). This was augmented using geometric transformation yielding 2312 plaques (1191 symptomatic and 1120 asymptomatic plaques). K10 (90% training and 10% testing) cross-validation DL protocol was implemented and showed an (i) accuracy and (ii) AUC without and with augmentation of 86.17%, 0.86 (p-value < 0.0001), and 89.7%, 0.91 (p-value < 0.0001), respectively. The DL characterization system consisted of validation of the two hypotheses: (a) mean feature strength (MFS) and (b) Mandelbrot's fractal dimension (FD) for measuring chaotic behavior. We demonstrated that both MFS and FD were higher in symptomatic plaques compared to asymptomatic plaques by 64.15 ± 0.73% (p-value < 0.0001) and 6 ± 0.13% (p-value < 0.0001), respectively. The benchmarking results show that DL with augmentation (ACC: 89.7%, AUC: 0.91 (p-value < 0.0001)) is superior to previously published machine learning (ACC: 83.7%) by 6.0%. The Atheromatic runs the test patient in < 2 s. Deep learning can be a useful tool for carotid ultrasound-based characterization and classification of symptomatic and asymptomatic plaques.

Integrative analysis for COVID-19 patient outcome prediction.

While image analysis of chest computed tomography (CT) for COVID-19 diagnosis has been intensively studied, little work has been performed for image-based patient outcome prediction. Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a majority of patients recover naturally. Therefore, an accurate prediction of disease progression with baseline imaging at the time of the initial presentation can help in patient management. In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission. To our knowledge, this is the first study that uses holistic information of a patient including both imaging and non-imaging data for outcome prediction. The proposed methods were thoroughly evaluated on datasets separately collected from three hospitals, one in the United States, one in Iran, and another in Italy, with a total 295 patients with reverse transcription polymerase chain reaction (RT-PCR) assay positive COVID-19 pneumonia. Our experimental results demonstrate that adding non-imaging features can significantly improve the performance of prediction to achieve AUC up to 0.884 and sensitivity as high as 96.1%, which can be valuable to provide clinical decision support in managing COVID-19 patients. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia. The source code of our work is available at https://github.com/DIAL-RPI/COVID19-ICUPrediction.

Automated stratification of liver disease in ultrasound: An online accurate feature classification paradigm.

PURPOSE: Fatty liver disease (FLD) is one of the most common diseases in liver. Early detection can improve the prognosis considerably. Using ultrasound for FLD detection is highly desirable due to its non-radiation nature, low cost and easy use. However, the results can be slow and ambiguous due to manual detection. The lack of computer trained systems leads to low image quality and inefficient disease classification. Thus, the current study proposes novel, accurate and reliable detection system for the FLD using computer-based training system. MATERIALS AND METHODS: One hundred twenty-four ultrasound sample images were selected retrospectively from a database of 62 patients consisting of normal and cancerous. The proposed training system was generated offline parameters using training liver image database. The classifier applied transformation parameters to an online system in order to facilitate real-time detection during the ultrasound scan. The system utilized six sets of features (a total of 128 features), namely Haralick, basic geometric, Fourier transform, discrete cosine transform, Gupta transform and Gabor transform. These features were extracted for both offline training and online testing. Levenberg-Marquardt back propagation network (BPN) classifier was used to classify the liver disease into normal and abnormal categories. RESULTS: Random partitioning approach was adapted to evaluate the classifier performance and compute its accuracy. Utilizing all the six sets of 128 features, the computer aided diagnosis (CAD) system achieved classification accuracy of 97.58%. Furthermore, the four performance metrics consisting of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) realized 98.08%, 97.22%, 96.23%, and 98.59%, respectively. CONCLUSION: The proposed system was successfully able to detect and classify the FLD. Furthermore, the proposed system was benchmarked against previous methods. The comparison established an advanced set of features in the Levenberg-Marquardt back propagation network reports a significant improvement compared to the existing techniques.