Deep learning method for aortic root detection

Background: Computed tomography angiography (CTA) is a preferred imaging technique for a wide range of vascular diseases. However, extensive manual analysis is required to detect and identify several anatomical landmarks for clinical application. This study demonstrates the feasibility of a fully automatic method for detecting the aortic root, which is a key anatomical landmark in this type of procedure. The approach is based on the use of deep learning techniques that attempt to mimic expert behavior. Methods: A total of 69 CTA scans (39 for training and 30 for validation) with different pathology types were selected to train the network. Furthermore, a total of 71 CTA scans were selected independently and applied as the test set to assess their performance. Results: The accuracy was evaluated by comparing the locations marked by the method with benchmark locations (which were manually marked by two experts). The interobserver error was 4.6 ± 2.3 mm. On an average, the differences between the locations marked by the two experts and those detected by the computer were 6.6 ± 3.0 mm and 6.8 ± 3.3 mm, respectively, when calculated using the test set. Conclusions: From an analysis of these results, we can conclude that the proposed method based on pre-trained CNN models can accurately detect the aortic root in CTA images without prior segmentationThis work was partially financed by Consellería de Cultura, Educación e Universidade (reference 2019–2021, ED431C 2018/19)S

Automatic detection of the aortic annular plane and coronary ostia from multidetector computed tomography

Anatomic landmark detection is crucial during preoperative planning of transcatheter aortic valve implantation (TAVI) to select the proper device size and assess the risk of complications. The detection is currently a time-consuming manual process influenced by the image quality and subject to operator variability. In this work, we propose a novel automatic method to detect the relevant aortic landmarks from MDCT images using deep learning techniques. We trained three convolutional neural networks (CNNs) with 344 multidetector computed tomography (MDCT) acquisitions to detect five anatomical landmarks relevant for TAVI planning: the three basal attachment points of the aortic valve leaflets and the left and right coronary ostia. The detection strategy used these three CNN models to analyse a single MDCT image and yield three segmentation volumes as output. These segmentation volumes were averaged into one final segmentation volume, and the final predicted landmarks were obtained during a postprocessing step. Finally, we constructed the aortic annular plane, defined by the three predicted hinge points, and measured the distances from this plane to the predicted coronary ostia (i.e., coronary height). The methodology was validated on 100 patients. The automatic landmark detection was able to detect all the landmarks and showed high accuracy as the median distance between the ground truth and predictions is lower than the interobserver variations (1.5 mm [1.1-2.1], 2.0 mm [1.3-2.8] with a paired difference -0.5 +/- 1.3 mm and p value <0.001). Furthermore, a high correlation is observed between predicted and manually measured coronary heights (for both R-2 = 0.8). The image analysis time per patient was below one second. The proposed method is accurate, fast, and reproducible. Embedding this tool based on deep learning in the preoperative planning routine may have an impact in the TAVI environments by reducing the time and cost and improving accuracy

Artificial intelligence and automation in valvular heart diseases

Artificial intelligence (AI) is gradually changing every aspect of social life, and healthcare is no exception. The clinical procedures that were supposed to, and could previously only be handled by human experts can now be carried out by machines in a more accurate and efficient way. The coming era of big data and the advent of supercomputers provides great opportunities to the development of AI technology for the enhancement of diagnosis and clinical decision-making. This review provides an introduction to AI and highlights its applications in the clinical flow of diagnosing and treating valvular heart diseases (VHDs). More specifically, this review first introduces some key concepts and subareas in AI. Secondly, it discusses the application of AI in heart sound auscultation and medical image analysis for assistance in diagnosing VHDs. Thirdly, it introduces using AI algorithms to identify risk factors and predict mortality of cardiac surgery. This review also describes the state-of-the-art autonomous surgical robots and their roles in cardiac surgery and intervention

TAVI-PREP: A Deep Learning-Based Tool for Automated Measurements Extraction in TAVI Planning

ABSTRACT: Background: Transcatheter aortic valve implantation (TAVI) is a less invasive alternative to open-heart surgery for treating severe aortic stenosis. Despite its benefits, the risk of procedural complications necessitates careful preoperative planning. Methods: This study proposes a fully automated deep learning-based method, TAVI-PREP, for pre-TAVI planning, focusing on measurements extracted from computed tomography (CT) scans. The algorithm was trained on the public MM-WHS dataset and a small subset of private data. It uses MeshDeformNet for 3D surface mesh generation and a 3D Residual U-Net for landmark detection. TAVI-PREP is designed to extract 22 different measurements from the aortic valvular complex. A total of 200 CT-scans were analyzed, and automatic measurements were compared to the ones made manually by an expert cardiologist. A second cardiologist analyzed 115 scans to evaluate inter-operator variability. Results: High Pearson correlation coefficients between the expert and the algorithm were obtained for most parameters (0.90–0.97), except for left and right coronary height (0.8 and 0.72, respectively). Similarly, the mean absolute relative error was within 5% for most measurements, except for left and right coronary height (11.6% and 16.5%, respectively). A greater consensus was observed among experts than when compared to the automatic approach, with TAVI-PREP showing no discernable bias towards either the lower or higher ends of the measurement spectrum. Conclusions: TAVI-PREP provides reliable and time-efficient measurements of the aortic valvular complex that could aid clinicians in the preprocedural planning of TAVI procedures

Multi-modality cardiac image computing: a survey

Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future

A current review of computational techniques for diseases characterizing associated with the aortic valve

En los últimos años, los avances en imagenología médica estan cambiado la forma de obtener información anatómica y funcional de las estructuras vinculadas con el corazón, particularmente, de las válvulas cardíacas. En este artículo se hace una revisión, que abarca el periodo 2014-2020, sobre las técnicas computacionales usadas en la caracterización, vía segmentación, de las enfermedades que afectan las mencionadas válvulas. La presente revisión proporciona información actualizada acerca de: a) enfermedades que afectan las válvulas, b) principales modalidades de adquisición de imágenes cardíacas, c) últimos avances en prótesis de válvulas aórticas empleadas en el implante valvular aórtico transcatéter (TAVI), d) técnicas usadas para la segmentación y caracterización de las válvulas. Los principales hallazgos indican que se destaca la tomografía computarizada para hacer una caracterización de la geometría y de la capacidad funcional de los principales tejidos de las válvulas; mientras que se ha proliferado el uso de prótesis, de última generación, las cuales tienden a disminuir las complicaciones clínicas posterior al remplazo de válvula y, a su vez, elevan la calidad de vida del paciente, razón por la cual el TAVI es cada vez más frecuente en pacientes de moderado y bajo riesgo quirúrgico.In recent years, advances in medical imaging have changed the way of obtaining anatomical and functional information on structures linked to the heart, particularly, the heart valves. In this article, a review is made, covering the period 2014-2020, on the computational techniques used in the characterization, via segmentation, of the diseases that affect the mentioned valves. This review provides updated information about: a) diseases affecting the valves, b) main cardiac imaging modalities, c) recent advances in aortic valve prostheses used in transcatheter aortic valve implantation (TAVI), d) techniques used for the segmentation and characterization of the valves. The main findings indicate that computed tomography is highlighted to characterize the geometry and functional capacity of the main valve tissues; while the use of state-of-the-art prostheses has proliferated, which tend to decrease clinical complications after valve replacement and, in turn, raise the patient’s quality of life, which is due TAVI is increasingly more frequent in patients of moderate and low surgical risk

Modelling blood flow in patients with heart valve disease using deep learning: A computationally efficient method to expand diagnostic capabilities in clinical routine

Introduction: The computational modelling of blood flow is known to provide vital hemodynamic parameters for diagnosis and treatment-support for patients with valvular heart disease. However, most diagnosis/treatment-support solutions based on flow modelling proposed utilize time- and resource-intensive computational fluid dynamics (CFD) and are therefore difficult to implement into clinical practice. In contrast, deep learning (DL) algorithms provide results quickly with little need for computational power. Thus, modelling blood flow with DL instead of CFD may substantially enhances the usability of flow modelling-based diagnosis/treatment support in clinical routine. In this study, we propose a DL-based approach to compute pressure and wall-shear-stress (WSS) in the aorta and aortic valve of patients with aortic stenosis (AS). Methods: A total of 103 individual surface models of the aorta and aortic valve were constructed from computed tomography data of AS patients. Based on these surface models, a total of 267 patient-specific, steady-state CFD simulations of aortic flow under various flow rates were performed. Using this simulation data, an artificial neural network (ANN) was trained to compute spatially resolved pressure and WSS using a centerline-based representation. An unseen test subset of 23 cases was used to compare both methods. Results: ANN and CFD-based computations agreed well with a median relative difference between both methods of 6.0% for pressure and 4.9% for wall-shear-stress. Demonstrating the ability of DL to compute clinically relevant hemodynamic parameters for AS patients, this work presents a possible solution to facilitate the introduction of modelling-based treatment support into clinical practice

Präzisionsmedizin in der Kinder- und Erwachsenenkardiologie - klinische Anwendung bildbasierter in silico Modellierung

Die richtige Therapie zum richtigen Zeitpunkt, nichtinvasiv und patientenindividuell zu identifizieren, ist das Ziel der Präzisionsmedizin. Durch den stetigen Fortschritt sowohl im Bereich der Bildgebung als auch in mathematischen Modellierungstechniken sowie einer zunehmenden Verfügbarkeit von leistungsstarker Informationstechnologie, gewinnen in silico (angelehnt an das Lateinische „in silicio“, also „in silicium“ bzw. im übertragenden Sinne im Computer ablaufende) Modellierungsverfahren eine immer größere Bedeutung auch im Bereich der kardiovaskulären Medizin. Die bildbasierte in silico Modellierung von Hämodynamik und Funktion des Herzens kann dabei einerseits helfen, die diagnostische Aussagekraft unterschiedlicher Bildgebungsmodalitäten zu erweitern, andererseits aber auch, verschiedene Parameter der postinterventionellen bzw. postoperativen Funktion vorherzusagen und so das geeignetste patientenindividuelle Therapieverfahren zu identifizieren. Im Bereich der pädiatrischen Kardiologie, insbesondere bei Patient*innen mit komplexen angeborenen Herzfehlern, ist eine individualisierte Therapieplanung zudem von ganz besonderer Bedeutung. Da die Anatomie des kardiovaskulären Systems in diesem Patientenkollektiv hoch individuell ist, gibt es häufig keine für das jeweilige Krankheitsbild einheitliche Therapie. Die virtuelle Behandlungsplanung bietet hier ein großes Potential für die multimodale Therapiefindung. Die Translation solcher Modellierungsansätze in die Klinik stellt jedoch eine große Hürde dar. Einerseits muss die Genauigkeit der jeweiligen Simulationsmethode quantifiziert und die Methode selbst validiert werden. Dafür benötigt es in der Regel eine hohe Anzahl an Patientendaten, die insbesondere in der Kinderkardiologie, aber auch aufgrund zunehmend strengerer Datenschutzrichtlinien häufig nicht zur Verfügung stehen. Andererseits sind die Simulationsverfahren sehr komplex und verlangen neben einer hohen technischen Expertise auch beachtliche Rechenkapazitäten und -laufzeiten, wodurch sich ihr routinemäßiger Einsatz in der Klinik ebenfalls verkompliziert. Das Problem der hohen Komplexität könnte durch den Einsatz künstlicher Intelligenz (KI) überwunden werden. Fehlende klinische Daten wiederum könnten mittels synthetischer Patientenkohorten augmentiert werden, sodass sowohl für mögliche Validierungsstudien als auch zum Trainieren des maschinellen Algorithmus‘ ein ausreichend großer Datensatz zur Verfügung stünde. In der vorliegenden Habilitationsschrift werden die Inhalte von fünf wissenschaftlichen Arbeiten zum Thema Präzisionsmedizin in der Kinder- und Erwachsenenkardiologie auf Grundlage bildbasierter in silico Modellierung vorgestellt. Dabei wird in Form einer Proof of Concept Studie die prinzipielle Durchführbarkeit der bildbasierten in silico Modellierung am Beispiel verschiedener Parameter der aortalen Hämodynamik gezeigt sowie die Validierung der Methodik gegen den klinischen Goldstandard des Herzkatheters präsentiert. An komplexen Patient*innen aus dem Bereich der Kinderkardiologie wird die bildbasierte in silico Modellierung für eine konkrete klinische Fragestellung angewandt. Zuletzt werden zwei Optimierungsansätze vorgestellt, die einerseits den komplexen Arbeitsablauf der bildbasierten in silico Modellierung mittels KI vereinfachen sowie andererseits das Problem der existierenden klinischen Datenlücken überwinden sollen

Artificial Intelligence and Transcatheter Interventions for Structural Heart Disease: A glance at the (near) future

With innovations in therapeutic technologies and changes in population demographics, transcatheter interventions for structural heart disease have become the preferred treatment and will keep growing. Yet, a thorough clinical selection and efficient pathway from diagnosis to treatment and follow-up are mandatory. In this review we reflect on how artificial intelligence may help to improve patient selection, pre-procedural planning, procedure execution and follow-up so to establish efficient and high quality health care in an increasing number of patients