A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool

Background: Cardiovascular disease (CVD) is challenging to diagnose and treat since symptoms appear late during the progression of atherosclerosis. Conventional risk factors alone are not always sufficient to properly categorize at-risk patients, and clinical risk scores are inadequate in predicting cardiac events. Integrating genomic-based biomarkers (GBBM) found in plasma/serum samples with novel non-invasive radiomics-based biomarkers (RBBM) such as plaque area, plaque burden, and maximum plaque height can improve composite CVD risk prediction in the pharmaceutical paradigm. These biomarkers consider several pathways involved in the pathophysiology of atherosclerosis disease leading to CVD. Objective: This review proposes two hypotheses: (i) The composite biomarkers are strongly correlated and can be used to detect the severity of CVD/Stroke precisely, and (ii) an explainable artificial intelligence (XAI)-based composite risk CVD/Stroke model with survival analysis using deep learning (DL) can predict in preventive, precision, and personalized (aiP 3 ) framework benefiting the pharmaceutical paradigm. Method: The PRISMA search technique resulted in 214 studies assessing composite biomarkers using radiogenomics for CVD/Stroke. The study presents a XAI model using AtheroEdge TM 4.0 to determine the risk of CVD/Stroke in the pharmaceutical framework using the radiogenomics biomarkers. Conclusions: Our observations suggest that the composite CVD risk biomarkers using radiogenomics provide a new dimension to CVD/Stroke risk assessment. The proposed review suggests a unique, unbiased, and XAI model based on AtheroEdge TM 4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm

A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool

Cardiovascular disease (CVD) is challenging to diagnose and treat since symptoms appear late during the progression of atherosclerosis. Conventional risk factors alone are not always sufficient to properly categorize at-risk patients, and clinical risk scores are inadequate in predicting cardiac events. Integrating genomic-based biomarkers (GBBM) found in plasma/serum samples with novel non-invasive radiomics-based biomarkers (RBBM) such as plaque area, plaque burden, and maximum plaque height can improve composite CVD risk prediction in the pharmaceutical paradigm. These biomarkers consider several pathways involved in the pathophysiology of atherosclerosis disease leading to CVD.This review proposes two hypotheses: (i) The composite biomarkers are strongly correlated and can be used to detect the severity of CVD/Stroke precisely, and (ii) an explainable artificial intelligence (XAI)-based composite risk CVD/Stroke model with survival analysis using deep learning (DL) can predict in preventive, precision, and personalized (aiP3) framework benefiting the pharmaceutical paradigm.The PRISMA search technique resulted in 214 studies assessing composite biomarkers using radiogenomics for CVD/Stroke. The study presents a XAI model using AtheroEdgeTM 4.0 to determine the risk of CVD/Stroke in the pharmaceutical framework using the radiogenomics biomarkers.Our observations suggest that the composite CVD risk biomarkers using radiogenomics provide a new dimension to CVD/Stroke risk assessment. The proposed review suggests a unique, unbiased, and XAI model based on AtheroEdgeTM 4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm

Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images

Polymeric endovascular implants are the next step in minimally invasive vascular interventions. As an alternative to traditional metallic drug-eluting stents, these often-erodible scaffolds present opportunities and challenges for patients and clinicians. Theoretically, as they resorb and are absorbed over time, they obviate the long-term complications of permanent implants, but in the short-term visualization and therefore positioning is problematic. Polymeric scaffolds can only be fully imaged using optical coherence tomography (OCT) imaging—they are relatively invisible via angiography—and segmentation of polymeric struts in OCT images is performed manually, a laborious and intractable procedure for large datasets. Traditional lumen detection methods using implant struts as boundary limits fail in images with polymeric implants. Therefore, it is necessary to develop an automated method to detect polymeric struts and luminal borders in OCT images; we present such a fully automated algorithm. Accuracy was validated using expert annotations on 1140 OCT images with a positive predictive value of 0.93 for strut detection and an R^2 correlation coefficient of 0.94 between detected and expert-annotated lumen areas. The proposed algorithm allows for rapid, accurate, and automated detection of polymeric struts and the luminal border in OCT images

Comparative assessment of texture features for the identification of cancer in ultrasound images: a review

In this paper, we review the use of texture features for cancer detection in Ultrasound (US) images of breast, prostate, thyroid, ovaries and liver for Computer-Aided Diagnosis (CAD) systems. This paper shows that texture features are a valuable tool to extract diagnostically relevant information from US images. This information helps practitioners to discriminate normal from abnormal tissues. A drawback of some classes of texture features comes from their sensitivity to both changes in image resolution and grayscale levels. These limitations pose a considerable challenge to CAD systems, because the information content of a specific texture feature depends on the US imaging system and its setup. Our review shows that single classes of texture features are insufficient, if considered alone, to create robust CAD systems, which can help to solve practical problems, such as cancer screening. Therefore, we recommend that the CAD system design involves testing a wide range of texture features along with features obtained with other image processing methods. Having such a competitive testing phase helps the designer to select the best feature combination for a particular problem. This approach will lead to practical US based cancer detection systems which de- liver real benefits to patients by improving the diagnosis accuracy while reducing health care cost

Μελέτη με οπτική συνεκτική τομογραφία των χαρακτηριστικών της αθηρωματικής πλάκας σε ασθενείς με οξύ έμφραγμα του μυοκαρδίου με ανάσπαση ST που έχουν υποβληθεί σε θρομβόλυση

Σκοπός: Εξετάσαμε σε ασθενείς με οξύ έμφραγμα του μυοκαρδίου με ανάσπαση ST (STEMI) τη συσχέτιση μεταξύ των μορφολογικών χαρακτηριστικών των ενόχων αθηρωματικών βλαβών, όπως αυτά εκτιμώνται με την οπτική συνεκτική τομογραφία (OCT) και με το βαθμό ροής κατά TIMI μετά τη θρομβόλυση. Αντικείμενο: Παρότι υπάρχουν διάφορες μεταβλητές οι οποίες έχουν βρεθεί ότι μπορεί να προβλέψουν την αποκατάσταση της ροής μετά τη χορήγηση θρομβολυτικής θεραπείας σε ασθενείς με STEMI, η επίδραση της μορφολογίας της ενόχου βλάβης δεν έχει μελετηθεί. Μεθοδολογία: Πενήντα πέντε ασθενείς με STEMI από 3 τριτοβάθμια κέντρα που υπεβλήθησαν σε θεραπεία επαναιμάτωσης με θρομβόλυση, και στους οποίους έγινε μελέτη της ενόχου βλάβης με OCT 24 έως 48 ώρες μετά τη θρομβόλυση συμπεριελήφθησαν στη μελέτη. Οι ασθενείς κατετάγησαν σε δύο ομάδες με βάση τη ροή κατά ΤΙΜΙ: ασθενείς με ροή TIMI III και ασθενείς με ροή TIMI II. Αποτελέσματα: Οι ασθενείς με ροή TIMI II είχαν πλάκες οι οποίες είχαν περισσότερα τεταρτημόρια με λιπιδικό πυρήνα σε σχέση με τους ασθενείς με ροή TIMI III (p<0,001), και είχαν μεγαλύτερη επίπτωση ρήξης της πλάκας (p=0,001). Το ελάχιστο πάχος της ινώδους κάψας ήταν μεγαλύτερο σε ασθενείς με βατές στεφανιαίες αρτηρίες σε σχέση με τους ασθενείς με επηρεασμένη ροή (87±26μm έναντι 48±18μm, p<0,0001). Το ελάχιστο πάχος της κάψας είχε ανεξάρτητη συσχέτιση με το βαθμό ροής κατά TIMI. Συμπεράσματα: Τα μορφολογικά χαρακτηριστικά της ενόχου αθηρωματικής βλάβης συσχετίζονται με τη στεφανιαία ροή μετά από τη θρομβόλυση. Η έκταση του λιπιδικού πυρήνα, η ύπαρξη ρήξης της πλάκας, και κυρίως το πάχος της ινώδους κάψας, συσχετίζονται με την έκβαση της θρομβόλυσης.Objectives: We investigated in patients with ST-elevation myocardial infarction (STEMI) the association between morphological characteristics of culprit atheromatic lesions as assessed by optical coherence tomography (OCT) and TIMI flow grade after thrombolysis. Background: Although several variables have been found to predict coronary flow after thrombolysis in patients with STEMI, the impact of culprit lesion morphology has not been studied. Methods: Fifty-five patients with STEMI from 3 tertiary centers that were treated with thrombolysis, and underwent OCT examination in the culprit lesion between 24-48 hours after thrombolysis were included in the study. Patients were categorized on the basis of TIMI flow grade into patients with TIMI flow III versus TIMI flow II. Results: Patients with TIMI flow II had plaques with more lipid quadrants than patients with TIMI flow III (p<0.001), and presented with greater incidence of plaque rupture (p=0.001). Mean minimal cap thickness was greater in patients with patent arteries than in patients with impaired flow (87±26μm versus 48±18μm, p<0.0001). Minimal cap thickness was independently associated with TIMI flow grade. Conclusions: The morphological characteristics of the culprit atheromatic lesion in patients with STEMI are associated with coronary flow after thrombolysis. The lipid content, the existence of rupture, and mainly the thickness of the fibrous cap, are associated with the outcome of thrombolysis

Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images

Polymeric endovascular implants are the next step in minimally invasive vascular interventions. As an alternative to traditional metallic drug-eluting stents, these often-erodible scaffolds present opportunities and challenges for patients and clinicians. Theoretically, as they resorb and are absorbed over time, they obviate the long-term complications of permanent implants, but in the short-term visualization and therefore positioning is problematic. Polymeric scaffolds can only be fully imaged using optical coherence tomography (OCT) imaging—they are relatively invisible via angiography—and segmentation of polymeric struts in OCT images is performed manually, a laborious and intractable procedure for large datasets. Traditional lumen detection methods using implant struts as boundary limits fail in images with polymeric implants. Therefore, it is necessary to develop an automated method to detect polymeric struts and luminal borders in OCT images; we present such a fully automated algorithm. Accuracy was validated using expert annotations on 1140 OCT images with a positive predictive value of 0.93 for strut detection and an R^2 correlation coefficient of 0.94 between detected and expert-annotated lumen areas. The proposed algorithm allows for rapid, accurate, and automated detection of polymeric struts and the luminal border in OCT images

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland