Automated Performance Assessment in Transoesophageal Echocardiography with Convolutional Neural Networks

Transoesophageal echocardiography (TEE) is a valuable diagnostic and monitoring imaging modality. Proper image acquisition is essential for diagnosis, yet current assessment techniques are solely based on manual expert review. This paper presents a supervised deep learning framework for automatically evaluating and grading the quality of TEE images. To obtain the necessary dataset, 38 participants of varied experience performed TEE exams with a high-fidelity virtual reality (VR) platform. Two Convolutional Neural Network (CNN) architectures, AlexNet and VGG, structured to perform regression, were finetuned and validated on manually graded images from three evaluators. Two different scoring strategies, a criteria-based percentage and an overall general impression, were used. The developed CNN models estimate the average score with a root mean square accuracy ranging between 84% − 93%, indicating the ability to replicate expert valuation. Proposed strategies for automated TEE assessment can have a significant impact on the training process of new TEE operators, providing direct feedback and facilitating the development of the necessary dexterous skills

Computer Vision in the Surgical Operating Room

Background: Multiple types of surgical cameras are used in modern surgical practice and provide a rich visual signal that is used by surgeons to visualize the clinical site and make clinical decisions. This signal can also be used by artificial intelligence (AI) methods to provide support in identifying instruments, structures, or activities both in real-time during procedures and postoperatively for analytics and understanding of surgical processes. Summary: In this paper, we provide a succinct perspective on the use of AI and especially computer vision to power solutions for the surgical operating room (OR). The synergy between data availability and technical advances in computational power and AI methodology has led to rapid developments in the field and promising advances. Key Messages: With the increasing availability of surgical video sources and the convergence of technologiesaround video storage, processing, and understanding, we believe clinical solutions and products leveraging vision are going to become an important component of modern surgical capabilities. However, both technical and clinical challenges remain to be overcome to efficiently make use of vision-based approaches into the clinic

임상의사 결정 지원 시스템을 위한 심층 신경망 기반의 심초음파 자동해석에 관한 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2022. 8. 김희찬.심초음파 검사는 심장병 진단에 사용되는 중요한 도구이며, 수축기 및 이완기 단계의 심장 이미지를 제공한다. 심초음파 검사를 통해 심방과 심실의 다양한 구조적 이상과, 판막 이상등의 질환을 정량적으로 또는 정성적으로 진단할 수 있다. 심초음파 검사는 비침습적인 특성으로 인하여에 심장 전문의들이 많이 사용하고 있으며, 심장 질환자가 점점 많아지는 추세에 따라 더 많이 사용될 것으로 기대되고 있다. 심초음파 검사는 이러한 안전성과 유용성에도 불구하고, CT나 MRI와는 달리 1)정확한 영상을 얻는데 오랜 훈련기간이 필요하고 2) 영상을 얻을 수 있는 부위와 얻을 수 잇는 단면영상이 제한적이어서 검사 시 놓친 소견은 추후 영상을 감수할 경우에도 발견할 수 없는 특징을 가지고 있다. 이에 다라 측정과 해석의 정량화와 함께 검사상 이상소견을 놓치지 않을 수 있는 보완조치에 대한 요구가 많았고, 이러한 요구에 부응하여 심장전문의를 위한 임상 의사결정 지원 시스템에 대한 많은 연구가 진행되고 있다.. 인공지능의 발달로 인해 어느정도 이러한 요구에 부응할 수 있게 되었다. 이 연구의 흐름은 두가지로 나뉘게 되는데, 첫째는 심장의 구조물들을 분할하여 크기를 측정하고 특이치를 감지하는 정량적인 연구방법과, 병변이 어느 부위에 있는지 이미지 내에서 확인하는 정성적 접근법으로 나뉜다. 기존에는 이 두 연구가 대부분 따로 진행되어 왔으나, 임상의사의 진단 흐름을 고려해 볼 때 이 두가지 모두가 포함되는 임상 의사 결정 지원 시스템의 개발이 필요한 현실이다. 이러한 관점에서 본 학위 논문의 목표는 대규모 코호트 후향적 연구를 통해 AI 기반의 심장 초음파 임상 의사결정 지원 시스템을 개발하고 검증하는 것이다. 데이터는 2016년에서 2021년도 사이에 서울대 병원에서 시행된 2600예의 심초음파검사 영상(정상소견1300명, 병적소견 1300명)를 이용하였다. 정량적분석과 정성적 분석을 모두 고려하기 위해 두개의 네트워크가 개발되었으며, 그 유효성은 환자 데이터로 검증되었다. 먼저 정량적 분석을 위한 이미지 분할을 위해 U-net 기반 딥러닝 네트워크가 개발되었으며, 개발에 필요한 데이터를 위해 심장전문의가 좌심실, 좌심방, 대동맥, 우심실, 좌심실 후벽 및 심실간 중격의 정보를 이미지에 표시를 하였다. 훈련된 네트워크로부터 나온 이미지로부터 6개의 구조물의 직경과 면적을 구하여 벡터화 하였으며, 수축기말 및 이완기말 단계의 프레임 정보를 벡터로부터 추출하였다. 둘째로 정성적 진단을 위한 네트워크 개발을 위해 Resnet152 기반의 CNN을 사용하였다. 이 네트워크의 입력데이터는 정량적 네트워크에서 추출된 수축기말 및 이완기말 정보를 기반으로 10프레임이 추출되었다. 입력데이터가 정상인지 아닌지 구분하도록 했을 뿐 아니라, 마지막 레이어에서 그라디언트 가중 클래스 활성화 매핑(Grad-CAM)방법론을 이용하여 네트워크가 이미지상의 어느 부위를 보고 이상소견으로 분류했는지 시각화 하였다. 그 결과 먼저 정량적 네트워크 성능을 측정하기 위해 환자 1300명의 데이터를 통해 각 구조물의 직경과 관련된 심장질환이 얼마나 잘 검출됐는지 확인하였다. 심실중격, 좌심실 후벽, 대동맥과 관련된 병적소견을 제외하고 다른구조물의 민감도와 특이성은 모두 90% 이상이다. 수축기 말기 및 확장기 말기 위상 검출도 정확했는데, 심장전문의에 의해 선택된 프레임에 비하여 수축기 말기의 경우 평균 0.52 프레임, 확장기 말기의 경우 0.9 프레임의 차이를 보였다. 정성분석을 위한 네트워크의 경우, 첫 번째 네트워크로부터 선택된 위상정보를 바탕으로 10개의 입력데이터를 결정하였고, 무작위로 선택된 10개의 결과를 비교하였다. 그 결과 정확도가 각각 90.33%, 81.16%로 나타났으며, 1차 정량적 네트워크 에서 추출된 수축기말, 이완기말 프레임 정보는 환자를 판별하는 네트워크의 성능 향상에 기여했음을 알 수 있다. 또한 Grad-CAM 결과는 첫 번째 네트워크의 프레임 정보를 기반으로 데이터에서 추출된10 장의 이미지가 입력데이터로 쓰였을 때가 무작위로 추출된 10장의 이미지로 훈련된 네트워크 보다 병변의 위치를 더 정확하게 표시하는 것을 확인하였다. 결론적으로 본 연구는 정량적, 정성적 분석을 위한 AI 기반 심장 초음파 임상의사 결정 지원 시스템을 개발하였으며, 이 시스템이 실현 가능한 것으로 검증되었다.Echocardiography is an indispensable tool for cardiologists in the diagnosis of heart diseases. By echocardiography, various structural abnormalities in the heart can be quantitatively or qualitatively diagnosed. Due to its non-invasiveness, the usage of echocardiography in the diagnosis of heart disease has continuously increased. Despite the increasing role in the cardiology practice, echocardiography requires experience in capturing and knowledge in interpreting images. . Moreover, in contrast to CT or MRI images, important information can be missed once not obtained at the time of examination. Therefore, obtaining and interpreting images should be done simultaneously, or, at least, all obtained images should be audited by the experienced cardiologist before releasing the patient from the examination booth. Because of the peculiar characteristics of echocardiography compared to CT or MRI, there have been incessant demands for the clinical decision support system(CDSS) for echocardiography. With the advance of Artificial Intelligence (AI), there have been several studies regarding decision support systems for echocardiography. The flow of these studies is divided into two approaches: One is the quantitative approach to segment the images and detects an abnormality in size and function. The other is the qualitative approach to detect abnormality in morphology. Unfortunately, most of these two studies have been conducted separately. However, since cardiologists perform quantitative and qualitative analysis simultaneously in analyzing echocardiography, an optimal CDSS needs to be a combination of these two approaches. From this point of view, this study aims to develop and validate an AI-based CDSS for echocardiograms through a large-scale retrospective cohort. Echocardiographic data of 2,600 patients who visited Seoul National University Hospital (1300 cardiac patients and 1300 non-cardiac patients with normal echocardiogram) between 2016 and 2021. Two networks were developed for the quantitative and qualitative analysis, and their usefulnesses were verified with the patient data. First, a U-net based deep learning network was developed for segmentation in the quantitative analysis. Annotated images by the experienced cardiologist with the left ventricle, interventricular septum, left ventricular posterior wall, right ventricle, aorta, and left atrium, were used for training. The diameters and areas of the six structures were obtained and vectorized from the segmentation images, and the frame information at the end-systolic and end-diastolic phases was extracted from the vector. The second network for the qualitative diagnosis was developed using a convolutional neural network (CNN) based on Resnet 152. The input data of this network was extracted from 10 frames of each patient based on end-diastolic and end-systolic phase information extracted from the quantitative network. The network not only distinguished the input data between normal and abnormal but also visualized the location of the abnormality on the image through the Gradient-weighted Class Activation Mapping (Grad-CAM) at the last layer. The performance of the quantitative network in the chamber size and function measurements was assessed in 1300 patients. Sensitivity and specificity were both over 90% except for pathologies related to the left ventricular posterior wall, interventricular septum, and aorta. The end-systolic and end-diastolic phase detection was also accurate, with an average difference of 0.52 frames for the end-systolic and 0.9 frames for the end-diastolic phases. In the case of the network for qualitative analysis, 10 input data were selected based on the phase information determined from the first network, and the results of 10 randomly selected images were compared. As a result, the accuracy was 90.3% and 81.2%, respectively, and the phase information selected from the first network contributed to the improvement of the performance of the network. Also, the results of Grad-CAM confirmed that the network trained with 10 images of data extracted based on the phase information from the first network displays the location of the lesion more accurately than the network trained with 10 randomly selected data. In conclusion, this study proposed an AI-based CDSS for echocardiography in the quantitative and qualitative analysis.ABSTRACT １ CONTENTS v LIST OF TABLES vii LIST OF FIGURES viii CHAPTER 1 1 Introduction 1 1. Introduction 2 1.1. Echocardiogram 2 1.1.1. Diagnosis using Echocardiogram 2 1.1.2. Limitation in Echocardiogram 3 1.1.3. Artificial Intelligence in Echocardiogram 6 1.2. Clinical Background 7 1.2.1. Diagnostic Flow 8 1.2.2. Previous studies and clinical implication of this study 11 1.3. Technical Background 16 1.3.1. Convolutional Neural Network (CNN) 16 1.3.1.1. U-net 18 1.3.1.2. Residual Network 20 1.3.1.3. Gradient-weighted Class Activation Mapping (Grad-CAM) 22 1.4. Unmet Clinical Needs 26 1.5. Objective 27 CHAPTER 2 28 Materials & Methods 28 2. Materials & Methods 29 2.1. Data Description 29 2.2. Annotated Data 32 2.3. Overall Architecture 33 2.3.1. Quantitative Network 35 2.3.2. Qualitative Network 37 2.4. Dice Similarity Score 39 2.5. Intersection over Union 40 CHAPTER 3 41 3. Results & Discussion 42 3.1. Quantitative Network Result 42 3.1.1. Diagnostic results 47 3.1.2. Phase Detection Result 49 3.2. Qualitative Network Results 51 3.2.1. Grad-CAM Result 56 3.3. Limitation 58 3.3.1. Need for external dataset for generalizable network 58 3.3.2. Futurework of the system 59 CHAPTER 4 60 4. Conclusion 61 Abstract in Korean 62 Bibliography 65박

Artificial intelligence and echocardiography

Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experience can lead to incorrect diagnoses. Artificial intelligence (AI) technologies provide new possibilities for echocardiography to generate accurate, consistent and automated interpretation of echocardiograms, thus potentially reducing the risk of human error. In this review, we discuss a subfield of AI relevant to image interpretation, called machine learning, and its potential to enhance the diagnostic performance of echocardiography. We discuss recent applications of these methods and future directions for AI-assisted interpretation of echocardiograms. The research suggests it is feasible to apply machine learning models to provide rapid, highly accurate and consistent assessment of echocardiograms, comparable to clinicians. These algorithms are capable of accurately quantifying a wide range of features, such as the severity of valvular heart disease or the ischaemic burden in patients with coronary artery disease. However, the applications and their use are still in their infancy within the field of echocardiography. Research to refine methods and validate their use for automation, quantification and diagnosis are in progress. Widespread adoption of robust AI tools in clinical echocardiography practice should follow and have the potential to deliver significant benefits for patient outcome

Automated assessment of echocardiographic image quality using deep convolutional neural networks

Myocardial ischemia tops the list of causes of death around the globe, but its diagnosis and early detection thrives on clinical echocardiography. Although echocardiography presents a huge advantage of a non-intrusive, low-cost point of care diagnosis, its image quality is inherently subjective with strong dependence on operators’ experience level and acquisition skill. In some countries, echo specialists are mandated to supplementary years of training to achieve ‘gold standard’ free-hand acquisition skill without which exacerbates the reliability of echocardiogram and increases possibility for misdiagnosis. These drawbacks pose significant challenges to adopting echocardiography as authoritative modalities for cardiac diagnosis. However, the prevailing and currently adopted solution is to manually carry out quality evaluation where an echocardiography specialist visually inspects several acquired images to make clinical decisions of its perceived quality and prognosis. This is a lengthening process and laced with variability of opinion consequently affection diagnostic responses. The goal of the research is to provide a multi-discipline, state-of-the-art solution that allows objective quality assessment of echocardiogram and to guarantee the reliability of clinical quantification processes. Computer graphic processing unit simulations, medical imaging analysis and deep convolutional neural network models were employed to achieve this goal. From a finite pool of echocardiographic patient datasets, 1650 random samples of echocardiogram cine-loops from different patients with age ranges from 17 and 85 years, who had undergone echocardiography between 2010 and 2020 were evaluated. We defined a set of pathological and anatomical criteria of image quality by which apical-four and parasternal long axis frames can be evaluated with feasibility for real-time optimization. The selected samples were annotated for multivariate model developments and validation of predicted quality score per frame. The outcome presents a robust artificial intelligence algorithm that indicate frames’ quality rating, real-time visualisation of element of quality and updates quality optimization in real-time. A prediction errors of 0.052, 0.062, 0.069, 0.056 for visibility, clarity, depth-gain, and foreshortening attributes were achieved, respectively. The model achieved a combined error rate of 3.6% with average prediction speed of 4.24 ms per frame. The novel method established a superior approach to two-dimensional image quality estimation, assessment, and clinical adequacy on acquisition of echocardiogram prior to quantification and diagnosis of myocardial infarction

Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of scars provide important information of the pathophysiology and progression of atrial fibrillation (AF). Hence, LA scar segmentation and quantification from LGE MRI can be useful in computer-assisted diagnosis and treatment stratification of AF patients. Since manual delineation can be time-consuming and subject to intra- and inter-expert variability, automating this computing is highly desired, which nevertheless is still challenging and under-researched. This paper aims to provide a systematic review on computing methods for LA cavity, wall, scar and ablation gap segmentation and quantification from LGE MRI, and the related literature for AF studies. Specifically, we first summarize AF-related imaging techniques, particularly LGE MRI. Then, we review the methodologies of the four computing tasks in detail, and summarize the validation strategies applied in each task. Finally, the possible future developments are outlined, with a brief survey on the potential clinical applications of the aforementioned methods. The review shows that the research into this topic is still in early stages. Although several methods have been proposed, especially for LA segmentation, there is still large scope for further algorithmic developments due to performance issues related to the high variability of enhancement appearance and differences in image acquisition.Comment: 23 page

A computer vision pipeline for fully automated echocardiogram interpretation

Cardiovascular disease is the leading cause of global mortality and continues to place a significant burden, in economic and resource terms, upon health services. A 2-dimensional transthoracic echocardiogram captures high spatial and temporal images and videos of the heart and is the modality of choice for the rapid assessment of heart function and structure due to it’s non-invasive nature and lack of ionising radiation. The challenging process of analysing echocardiographic images is currently manually performed by trained experts, though this process is vulnerable to intra- and inter-observer variability and is highly time-consuming. Additionally, echocardiographic images suffer from varying degrees of noise and vary drastically in terms of image quality. Exponential advancements in the fields of artificial intelligence, deep learning and computer vision have enabled the rapid development of automated systems capable of high-precision tasks, often out-performing human experts. This thesis aims to investigate the applicability of applying deep learning methods to automate key processes in the modern echocardiographic laboratory. Namely, view classification, quality assessment, cardiac phase detection, segmentation of the left ventricle and keypoint detection on tissue Doppler imaging strips. State-of-the-art deep learning architectures were applied to each task, and evaluated against ground-truth annotations provided by trained experts. The datasets used throughout each Chapter are diverse and, in some cases, have been made public for the benefit of the research community. To encourage transparency and openness, all code and model weights have been published. Should automated deep learning systems, both online (in terms of providing real-time feedback) and offline (behind the scenes), become integrated within clinical practice, there is great potential for improved accuracy and efficiency, thus improving patient outcomes. Furthermore, health services could save valuable resources such as time and money

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