Artificial Intelligence and Echocardiography

Artificial intelligence (AI) is evolving in the field of diagnostic medical imaging, including echocardiography. Although the dynamic nature of echocardiography presents challenges beyond those of static images from X-ray, computed tomography, magnetic resonance, and radioisotope imaging, AI has influenced all steps of echocardiography, from image acquisition to automatic measurement and interpretation. Considering that echocardiography often is affected by inter-observer variability and shows a strong dependence on the level of experience, AI could be extremely advantageous in minimizing observer variation and providing reproducible measures, enabling accurate diagnosis. Currently, most reported AI applications in echocardiographic measurement have focused on improved image acquisition and automation of repetitive and tedious tasks; however, the role of AI applications should not be limited to conventional processes. Rather, AI could provide clinically important insights from subtle and non-specific data, such as changes in myocardial texture in patients with myocardial disease. Recent initiatives to develop large echocardiographic databases can facilitate development of AI applications. The ultimate goal of applying AI to echocardiography is automation of the entire process of echocardiogram analysis. Once automatic analysis becomes reliable, workflows in clinical echocardiographic will change radically. The human expert will remain the master controlling the overall diagnostic process, will not be replaced by AI, and will obtain significant support from AI systems to guide acquisition, perform measurements, and integrate and compare data on request.ope

Automated multi-beat tissue Doppler echocardiography analysis using deep neural networks

Tissue Doppler imaging is an essential echocardiographic technique for the non-invasive assessment of myocardial blood velocity. Image acquisition and interpretation are performed by trained operators who visually localise landmarks representing Doppler peak velocities. Current clinical guidelines recommend averaging measurements over several heartbeats. However, this manual process is both time-consuming and disruptive to workflow. An automated system for accurate beat isolation and landmark identification would be highly desirable. A dataset of tissue Doppler images was annotated by three cardiologist experts, providing a gold standard and allowing for observer variability comparisons. Deep neural networks were trained for fully automated predictions on multiple heartbeats and tested on tissue Doppler strips of arbitrary length. Automated measurements of peak Doppler velocities show good Bland–Altman agreement (average standard deviation of 0.40 cm/s) with consensus expert values; less than the inter-observer variability (0.65 cm/s). Performance is akin to individual experts (standard deviation of 0.40 to 0.75 cm/s). Our approach allows for > 26 times as many heartbeats to be analysed, compared to a manual approach. The proposed automated models can accurately and reliably make measurements on tissue Doppler images spanning several heartbeats, with performance indistinguishable from that of human experts, but with significantly shorter processing time

Multibeat echocardiographic phase detection using deep neural networks

Background Accurate identification of end-diastolic and end-systolic frames in echocardiographic cine loops is important, yet challenging, for human experts. Manual frame selection is subject to uncertainty, affecting crucial clinical measurements, such as myocardial strain. Therefore, the ability to automatically detect frames of interest is highly desirable. Methods We have developed deep neural networks, trained and tested on multi-centre patient data, for the accurate identification of end-diastolic and end-systolic frames in apical four-chamber 2D multibeat cine loop recordings of arbitrary length. Seven experienced cardiologist experts independently labelled the frames of interest, thereby providing infallible annotations, allowing for observer variability measurements. Results When compared with the ground-truth, our model shows an average frame difference of −0.09 ± 1.10 and 0.11 ± 1.29 frames for end-diastolic and end-systolic frames, respectively. When applied to patient datasets from a different clinical site, to which the model was blind during its development, average frame differences of −1.34 ± 3.27 and −0.31 ± 3.37 frames were obtained for both frames of interest. All detection errors fall within the range of inter-observer variability: [-0.87, −5.51]±[2.29, 4.26] and [-0.97, −3.46]±[3.67, 4.68] for ED and ES events, respectively. Conclusions The proposed automated model can identify multiple end-systolic and end-diastolic frames in echocardiographic videos of arbitrary length with performance indistinguishable from that of human experts, but with significantly shorter processing time

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