4,874 research outputs found
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
Visually grounded learning of keyword prediction from untranscribed speech
During language acquisition, infants have the benefit of visual cues to
ground spoken language. Robots similarly have access to audio and visual
sensors. Recent work has shown that images and spoken captions can be mapped
into a meaningful common space, allowing images to be retrieved using speech
and vice versa. In this setting of images paired with untranscribed spoken
captions, we consider whether computer vision systems can be used to obtain
textual labels for the speech. Concretely, we use an image-to-words multi-label
visual classifier to tag images with soft textual labels, and then train a
neural network to map from the speech to these soft targets. We show that the
resulting speech system is able to predict which words occur in an
utterance---acting as a spoken bag-of-words classifier---without seeing any
parallel speech and text. We find that the model often confuses semantically
related words, e.g. "man" and "person", making it even more effective as a
semantic keyword spotter.Comment: 5 pages, 3 figures, 5 tables; small updates, added link to code;
accepted to Interspeech 201
Estimating and understanding motion : from diagnostic to robotic surgery
Estimating and understanding motion from an image sequence is a central topic in computer vision. The high interest in this topic is because we are living in a world where many events that occur in the environment are dynamic. This makes motion estimation and understanding a natural component and a key factor in a widespread of applications including object recognition , 3D shape reconstruction, autonomous navigation and medica! diagnosis.
Particularly, we focus on the medical domain in which understanding the human body for clinical purposes requires retrieving the organs' complex motion patterns, which is in general a hard problem when using only image data. In this thesis, we cope with this problem by posing the question - How to achieve a realistic motion estimation to offer a better clinical understanding? We focus this thesis on answering this question by using a variational formulation as a basis to understand one of the most complex motions in the human's body, the heart motion, through three different applications: (i) cardiac motion estimation for diagnostic, (ii) force estimation and (iii) motion prediction, both for robotic surgery.
Firstly, we focus on a central topic in cardiac imaging that is the estimation of the cardiac motion. The main aim is to offer objective and understandable measures to physicians for helping them in the diagnostic of cardiovascular diseases. We employ ultrafast ultrasound data and tools for imaging motion drawn from diverse areas such as low-rank analysis and variational deformation to perform a realistic cardiac motion estimation. The significance is that by taking low-rank data with carefully chosen penalization, synergies in this complex variational problem can be created. We demonstrate how our proposed solution deals with complex deformations through careful numerical experiments using realistic and simulated data.
We then move from diagnostic to robotic surgeries where surgeons perform delicate procedures remotely through robotic manipulators without directly interacting with the patients. As a result, they lack force feedback, which is an important primary sense for increasing surgeon-patient transparency and avoiding injuries and high mental workload. To solve this problem, we follow the conservation principies of continuum mechanics in which it is clear that the change in shape of an elastic object is directly proportional to the force applied. Thus, we create a variational framework to acquire the deformation that the tissues undergo due to an applied force. Then, this information is used in a learning system to find the nonlinear relationship between the given data and the applied force. We carried out experiments with in-vivo and ex-vivo data and combined statistical, graphical and perceptual analyses to demonstrate the strength of our solution.
Finally, we explore robotic cardiac surgery, which allows carrying out complex procedures including Off-Pump Coronary Artery Bypass Grafting (OPCABG). This procedure avoids the associated complications of using Cardiopulmonary Bypass (CPB) since the heart is not arrested while performing the surgery on a beating heart. Thus, surgeons have to deal with a dynamic target that compromisetheir dexterity and the surgery's precision. To compensate the heart motion, we propase a solution composed of three elements: an energy function to estimate the 3D heart motion, a specular highlight detection strategy and a prediction approach for increasing the robustness of the solution. We conduct evaluation of our solution using phantom and realistic datasets.
We conclude the thesis by reporting our findings on these three applications and highlight the dependency between motion estimation and motion understanding at any dynamic event, particularly in clinical scenarios.L’estimació i comprensió del moviment dins d’una seqüència d’imatges és un tema central en la visió per ordinador, el que genera un gran interès perquè vivim en un entorn ple d’esdeveniments dinà mics. Per aquest motiu és considerat com un component natural i factor clau dins d’un ampli ventall d’aplicacions, el qual inclou el reconeixement d’objectes, la reconstrucció de formes tridimensionals, la navegació autònoma i el diagnòstic de malalties.
En particular, ens situem en l’à mbit mèdic en el qual la comprensió del cos humà , amb finalitats clÃniques, requereix l’obtenció de patrons complexos de moviment dels òrgans. Aquesta és, en general, una tasca difÃcil quan s’utilitzen només dades de tipus visual. En aquesta tesi afrontem el problema plantejant-nos la pregunta - Com es pot aconseguir una estimació realista del moviment amb l’objectiu d’oferir una millor comprensió clÃnica? La tesi se centra en la resposta mitjançant l’ús d’una formulació variacional com a base per entendre un dels moviments més complexos del cos humà , el del cor, a través de tres aplicacions: (i) estimació del moviment cardÃac per al diagnòstic, (ii) estimació de forces i (iii) predicció del moviment, orientant-se les dues últimes en cirurgia robòtica.
En primer lloc, ens centrem en un tema principal en la imatge cardÃaca, que és l’estimació del moviment cardÃac. L’objectiu principal és oferir als metges mesures objectives i comprensibles per ajudar-los en el diagnòstic de les malalties cardiovasculars. Fem servir dades d’ultrasons ultrarà pids i eines per al moviment d’imatges procedents de diverses à rees, com ara l’anà lisi de baix rang i la deformació variacional, per fer una estimació realista del moviment cardÃac. La importà ncia rau en que, en prendre les dades de baix rang amb una penalització acurada, es poden crear sinergies en aquest problema variacional complex. Mitjançant acurats experiments numèrics, amb dades realÃstiques i simulades, hem demostrat com les nostres propostes solucionen deformacions complexes.
Després passem del diagnòstic a la cirurgia robòtica, on els cirurgians realitzen procediments delicats remotament, a través de manipuladors robòtics, sense interactuar directament amb els pacients. Com a conseqüència, no tenen la percepció de la força com a resposta, que és un sentit primari important per augmentar la transparència entre el cirurgià i el pacient, per evitar lesions i per
reduir la cà rrega de treball mental. Resolem aquest problema seguint els principis de conservació de la mecà nica del medi continu, en els quals està clar que el canvi en la forma d’un objecte elà stic és directament proporcional a la força aplicada. Per això hem creat un marc variacional que adquireix la deformació que pateixen els teixits per l’aplicació d’una força. Aquesta informació s’utilitza en un sistema d’aprenentatge, per trobar la relació no lineal entre les dades donades i la força aplicada. Hem dut a terme experiments amb dades in-vivo i ex-vivo i hem combinat l’anà lisi estadÃstic, grà fic i de percepció que demostren la robustesa de la nostra solució. Finalment, explorem la cirurgia cardÃaca robòtica, la qual cosa permet realitzar procediments complexos, incloent la cirurgia coronà ria sense bomba (off-pump coronary artery bypass grafting o OPCAB). Aquest procediment evita les complicacions associades a l’ús de circulació extracorpòria (Cardiopulmonary Bypass o CPB), ja que el cor no s’atura mentre es realitza la cirurgia. Això
comporta que els cirurgians han de tractar amb un objectiu dinà mic que compromet la seva destresa i la precisió de la cirurgia. Per compensar el moviment del cor, proposem una solució composta de tres elements: un funcional d’energia per estimar el moviment tridimensional del cor, una estratègia de detecció de les reflexions especulars i una aproximació basada en mètodes de predicció, per tal d’augmentar la robustesa de la solució. L’avaluació de la nostra solució s’ha dut
a terme mitjançant conjunts de dades sintètiques i realistes.
La tesi conclou informant dels nostres resultats en aquestes tres aplicacions i posant de relleu la dependència entre l’estimació i la comprensió del moviment en qualsevol esdeveniment dinà mic, especialment en escenaris clÃnics.Postprint (published version
Semi-supervised source localization in reverberant environments with deep generative modeling
We propose a semi-supervised approach to acoustic source localization in
reverberant environments based on deep generative modeling. Localization in
reverberant environments remains an open challenge. Even with large data
volumes, the number of labels available for supervised learning in reverberant
environments is usually small. We address this issue by performing
semi-supervised learning (SSL) with convolutional variational autoencoders
(VAEs) on reverberant speech signals recorded with microphone arrays. The VAE
is trained to generate the phase of relative transfer functions (RTFs) between
microphones, in parallel with a direction of arrival (DOA) classifier based on
RTF-phase. These models are trained using both labeled and unlabeled RTF-phase
sequences. In learning to perform these tasks, the VAE-SSL explicitly learns to
separate the physical causes of the RTF-phase (i.e., source location) from
distracting signal characteristics such as noise and speech activity. Relative
to existing semi-supervised localization methods in acoustics, VAE-SSL is
effectively an end-to-end processing approach which relies on minimal
preprocessing of RTF-phase features. As far as we are aware, our paper presents
the first approach to modeling the physics of acoustic propagation using deep
generative modeling. The VAE-SSL approach is compared with two signal
processing-based approaches, steered response power with phase transform
(SRP-PHAT) and MUltiple SIgnal Classification (MUSIC), as well as fully
supervised CNNs. We find that VAE-SSL can outperform the conventional
approaches and the CNN in label-limited scenarios. Further, the trained VAE-SSL
system can generate new RTF-phase samples, which shows the VAE-SSL approach
learns the physics of the acoustic environment. The generative modeling in
VAE-SSL thus provides a means of interpreting the learned representations.Comment: Revision, submitted to IEEE Acces
Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review
Accurate tissue differentiation during orthopedic and neurological surgeries is critical, given that such surgeries involve operations on or in the vicinity of vital neurovascular structures and erroneous surgical maneuvers can lead to surgical complications. By now, the number of emerging technologies tackling the problem of intraoperative tissue classification methods is increasing. Therefore, this systematic review paper intends to give a general overview of existing technologies. The review was done based on the PRISMA principle and two databases: PubMed and IEEE Xplore. The screening process resulted in 60 full-text papers. The general characteristics of the methodology from extracted papers included data processing pipeline, machine learning methods if applicable, types of tissues that can be identified with them, phantom used to conduct the experiment, and evaluation results. This paper can be useful in identifying the problems in the current status of the state-of-the-art intraoperative tissue classification methods and designing new enhanced techniques
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