Multimodality Risk Assessment of Patients with Ischemic Heart Disease Using Deep Learning Models Applied to Electrocardiograms and Chest X-rays.

Comprehensive management approaches for patients with ischemic heart disease (IHD) are important aids for prognostication and treatment planning. While single-modality deep neural networks (DNNs) have shown promising performance for detecting cardiac abnormalities, the potential benefits of using DNNs for multimodality risk assessment in patients with IHD have not been reported. The purpose of this study was to investigate the effectiveness of multimodality risk assessment in patients with IHD using a DNN that utilizes 12-lead electrocardiograms (ECGs) and chest X-rays (CXRs), with the prediction of major adverse cardiovascular events (MACEs) being of particular concern.DNN models were applied to detection of left ventricular systolic dysfunction (LVSD) on ECGs and identification of cardiomegaly findings on CXRs. A total of 2107 patients who underwent elective percutaneous coronary intervention were categorized into 4 groups according to the models' outputs: Dual-modality high-risk (n = 105), ECG high-risk (n = 181), CXR high-risk (n = 392), and No-risk (n = 1,429).A total of 342 MACEs were observed. The incidence of a MACE was the highest in the Dual-modality high-risk group (P < 0.001). Multivariate Cox hazards analysis for predicting MACE revealed that the Dual-modality high-risk group had a significantly higher risk of MACE than the No-risk group (hazard ratio (HR): 2.370, P < 0.001), the ECG high-risk group (HR: 1.906, P = 0.010), and the CXR high-risk group (HR: 1.624, P = 0.018), after controlling for confounding factors.The results suggest the usefulness of multimodality risk assessment using DNN models applied to 12-lead ECG and CXR data from patients with IHD.

Deep Learning Models for Predicting Left Heart Abnormalities From Single-Lead Electrocardiogram for the Development of Wearable Devices.

BACKGROUND: Left heart abnormalities are risk factors for heart failure. However, echocardiography is not always available. Electrocardiograms (ECGs), which are now available from wearable devices, have the potential to detect these abnormalities. Nevertheless, whether a model can detect left heart abnormalities from single Lead I ECG data remains unclear.Methods and Results: We developed Lead I ECG models to detect low ejection fraction (EF), wall motion abnormality, left ventricular hypertrophy (LVH), left ventricular dilatation, and left atrial dilatation. We used a dataset comprising 229,439 paired sets of ECG and echocardiography data from 8 facilities, and validated the model using external verification with data from 2 facilities. The area under the receiver operating characteristic curves of our model was 0.913 for low EF, 0.832 for wall motion abnormality, 0.797 for LVH, 0.838 for left ventricular dilatation, and 0.802 for left atrial dilatation. In interpretation tests with 12 cardiologists, the accuracy of the model was 78.3% for low EF and 68.3% for LVH. Compared with cardiologists who read the 12-lead ECGs, the model's performance was superior for LVH and similar for low EF. CONCLUSIONS: From a multicenter study dataset, we developed models to predict left heart abnormalities using Lead I on the ECG. The Lead I ECG models show superior or equivalent performance to cardiologists using 12-lead ECGs.

Automatic Detection of Left Ventricular Dilatation and Hypertrophy from Electrocardiograms Using Deep Learning.

Left ventricular dilatation (LVD) and left ventricular hypertrophy (LVH) are risk factors for heart failure, and their detection improves heart failure screening. This study aimed to investigate the ability of deep learning to detect LVD and LVH from a 12-lead electrocardiogram (ECG). Using ECG and echocardiographic data, we developed deep learning and machine learning models to detect LVD and LVH. We also examined conventional ECG criteria for the diagnosis of LVH. We calculated the area under the receiver operating characteristic (AUROC) curve, sensitivity, specificity, and accuracy of each model and compared the performance of the models. We analyzed data for 18,954 patients (mean age (standard deviation): 64.2 (16.5) years, men: 56.7%). For the detection of LVD, the value (95% confidence interval) of the AUROC was 0.810 (0.801-0.819) for the deep learning model, and this was significantly higher than that of the logistic regression and random forest methods (P < 0.001). The AUROCs for the logistic regression and random forest methods (machine learning models) were 0.770 (0.761-0.779) and 0.757 (0.747-0.767), respectively. For the detection of LVH, the AUROC was 0.784 (0.777-0.791) for the deep learning model, and this was significantly higher than that of the logistic regression and random forest methods and conventional ECG criteria (P < 0.001). The AUROCs for the logistic regression and random forest methods were 0.758 (0.751-0.765) and 0.716 (0.708-0.724), respectively. This study suggests that deep learning is a useful method to detect LVD and LVH from 12-lead ECGs.

Comparison of post-stent optical coherence tomography findings: Layered versus non-layered culprit lesions.

OBJECTIVES: This study aimed to investigate the vascular response of lesions with a layered phenotype. BACKGROUND: Recent studies have shown that layered plaques at culprit lesions detected by optical coherence tomography (OCT) have greater plaque burden and more inflammatory features than non-layered plaques. METHODS: This is a retrospective observational study. A total of 193 target lesions from 193 patients [100 patients with acute coronary syndromes (ACS) and 93 with stable angina pectoris (SAP)] who had undergone OCT imaging of the culprit lesion both before and after stenting were included. Layered plaques were identified by OCT as plaques with layers of different optical density. Patients were divided into two groups based on the presence or absence of a layered phenotype at the culprit lesion, and pre- and post-procedure OCT findings were compared. RESULTS: Among 193 patients, 36 (36.0%) lesions in ACS patients and 56 (60.2%) lesions in SAP patients were found to have a layered phenotype at the culprit lesion. At baseline, percent area stenosis was greater in layered plaque than in non-layered plaque (p = .019). Following stent implantation, the stent expansion ratio and mean stent eccentricity index were significantly lower in layered plaques than in non-layered plaques (p = .041, p = .017, respectively), mainly derived from ACS patients. CONCLUSION: Following stent implantation, plaques with a layered phenotype had less stent expansion and more eccentric lumens. Aggressive balloon dilation may be required to obtain optimal stent outcomes in patients with a layered plaque phenotype at the culprit lesion.

Healed Plaques in Patients With Stable Angina Pectoris.

OBJECTIVE: Healed plaques, signs of previous plaque destabilization, are frequently found in the coronary arteries. Healed plaques can now be diagnosed in living patients. We investigated the prevalence, angiographic, and optical coherence tomography features of healed plaques in patients with stable angina pectoris. Approach and Results: Patients with stable angina pectoris who had undergone optical coherence tomography imaging were included. Healed plaques were defined as plaques with one or more signal-rich layers of different optical density. Patients were divided into 2 groups based on layered or nonlayered phenotype at the culprit lesion. Among 163 patients, 87 (53.4%) had layered culprit plaque. Patients with layered culprit plaque had more multivessel disease (62.1% versus 44.7%, P=0.027) and more angiographically complex culprit lesions (64.4% versus 35.5%, P<0.001). Layered culprit plaques had higher prevalence of lipid plaque (83.9% versus 64.5%, P=0.004), macrophage infiltration (58.6% versus 35.5%, P=0.003), calcifications (78.2% versus 63.2%, P=0.035), and thrombus (28.7% versus 14.5%, P=0.029). Lipid index (P=0.001) and percent area stenosis (P=0.015) were greater in the layered group. The number of nonculprit plaques, evaluated using coronary angiograms, tended to be greater in patients with layered culprit plaque (4.2±2.5 versus 3.5±2.1, P=0.053). Nonculprit plaques in patients with layered culprit lesion had higher prevalence of layered pattern (P=0.002) and lipid phenotype (P=0.005). Lipid index (P=0.013) and percent area stenosis (P=0.002) were also greater in this group. CONCLUSIONS: In patients with stable angina pectoris, healed culprit plaques are common and have more features of vulnerability and advanced atherosclerosis both at culprit and nonculprit lesions.

Deep Learning Algorithm to Detect Cardiac Sarcoidosis From Echocardiographic Movies.

BACKGROUND: Because the early diagnosis of subclinical cardiac sarcoidosis (CS) remains difficult, we developed a deep learning algorithm to distinguish CS patients from healthy subjects using echocardiographic movies.Methods and Results:Among the patients who underwent echocardiography from January 2015 to December 2019, we chose 151 echocardiographic movies from 50 CS patients and 151 from 149 healthy subjects. We trained two 3D convolutional neural networks (3D-CNN) to identify CS patients using a dataset of 212 echocardiographic movies with and without a transfer learning method (Pretrained algorithm and Non-pretrained algorithm). On an independent set of 41 echocardiographic movies, the area under the receiver-operating characteristic curve (AUC) of the Pretrained algorithm was greater than that of Non-pretrained algorithm (0.842, 95% confidence interval (CI): 0.722-0.962 vs. 0.724, 95% CI: 0.566-0.882, P=0.253). The AUC from the interpretation of the same set of 41 echocardiographic movies by 5 cardiologists was not significantly different from that of the Pretrained algorithm (0.855, 95% CI: 0.735-0.975 vs. 0.842, 95% CI: 0.722-0.962, P=0.885). A sensitivity map demonstrated that the Pretrained algorithm focused on the area of the mitral valve. CONCLUSIONS: A 3D-CNN with a transfer learning method may be a promising tool for detecting CS using an echocardiographic movie.

The Effectiveness of a Deep Learning Model to Detect Left Ventricular Systolic Dysfunction from Electrocardiograms.

Deep learning models can be applied to electrocardiograms (ECGs) to detect left ventricular (LV) dysfunction. We hypothesized that applying a deep learning model may improve the diagnostic accuracy of cardiologists in predicting LV dysfunction from ECGs. We acquired 37,103 paired ECG and echocardiography data records of patients who underwent echocardiography between January 2015 and December 2019. We trained a convolutional neural network to identify the data records of patients with LV dysfunction (ejection fraction < 40%) using a dataset of 23,801 ECGs. When tested on an independent set of 7,196 ECGs, we found the area under the receiver operating characteristic curve was 0.945 (95% confidence interval: 0.936-0.954). When 7 cardiologists interpreted 50 randomly selected ECGs from the test dataset of 7,196 ECGs, their accuracy for predicting LV dysfunction was 78.0% ± 6.0%. By referring to the model's output, the cardiologist accuracy improved to 88.0% ± 3.7%, which indicates that model support significantly improved the cardiologist diagnostic accuracy (P = 0.02). A sensitivity map demonstrated that the model focused on the QRS complex when detecting LV dysfunction on ECGs. We developed a deep learning model that can detect LV dysfunction on ECGs with high accuracy. Furthermore, we demonstrated that support from a deep learning model can help cardiologists to identify LV dysfunction on ECGs.

Clinical significance of healed plaque detected by optical coherence tomography: a 2-year follow-up study.

Recent studies have shown that healed plaque at the culprit lesion detected by optical coherence tomography (OCT) is a sign of pan-vascular vulnerability and advanced atherosclerosis. However, the clinical significance of healed plaque is unknown. A total of 265 patients who had OCT imaging of a culprit vessel and 2-year clinical follow-up data were included. Patients were stratified based on the presence or absence of a layered plaque phenotype, defined as layers of different optical density by OCT at either culprit or non-culprit lesions. The association between layered plaque and major adverse cardiac events (MACE), defined as cardiac death, acute coronary syndromes (ACS), or revascularization, was studied. Among 265 patients, 96 (36.2%) had the layered plaque phenotype. Layered plaque was more frequently observed in stable angina pectoris patients than in ACS patients (57.8%vs. 25.1%, p < 0.001). The average clinical follow-up period was 672 ± 172 days. Cumulative MACE was significantly higher in patients with layered plaque (p = 0.041), which was primarily driven by the high revascularization rate at 2 years (p = 0.002). Multivariate regression analysis showed that presence of layered plaque and low-density lipoprotein cholesterol levels were independently associated with an increased risk of revascularization (p = 0.026, p = 0.008, respectively). Patients with healed plaque in the culprit vessel had a higher incidence of revascularization, as compared to those without healed plaque, at 2 years.

Predictors for layered coronary plaques: an optical coherence tomography study.

Healed coronary plaques, morphologically characterized by a layered pattern, are signatures of previous plaque disruption and healing. Recent optical coherence tomography (OCT) studies showed that layered plaque is associated with vascular vulnerability. However, factors associated with layered plaques have not been studied. The aim of this study was to investigate predictors for layered plaque at the culprit plaques and at non-culprit plaques. Patients with coronary artery disease who underwent pre-intervention OCT imaging of the culprit lesion were included. Layered plaques were defined as plaques with one or more layers of different optical density and a clear demarcation from underlying components. Among 313 patients, layered plaque at the culprit lesion was observed in 18.8% of ST-segment elevation myocardial infarction patients, 36.3% of non-ST-segment elevation acute coronary syndrome patients, and 53.4% of stable angina pectoris (SAP) patients (p < 0.001). In the multivariable model, SAP, multivessel disease, type B2/C lesion, and diameter stenosis > 70% were independent predictors for layered plaque at the culprit lesion. In addition, 394 non-culprit plaques in 190 patients were assessed to explore predictors for layered plaques at non-culprit lesions. SAP, and thin-cap fibroatheroma and layered plaque at the culprit lesion were independent predictors for layered plaques at non-culprit lesions. In conclusion, clinical presentation of SAP was a strong predictor for layered plaque at both culprit plaques and non-culprit plaques. Development and biologic significance of layered plaques may be related to a balance between pan-vascular vulnerability and endogenous anti-thrombotic protective mechanism.

Efficacy of Fractional Flow Reserve-Guided Percutaneous Cornary Intervention for Patients with Angina Pectoris.

Evaluation of hemodynamic parameters, such as fractional flow reserve (FFR), is recommended before percutaneous coronary intervention (PCI) for patients with angina pectoris (AP). However, the advantage of FFR-guided PCI has not been fully established. This study was performed to confirm whether FFR-guided PCI improves the prognosis compared with other treatments. Multiple databases were searched for studies published from 2000 to 2018, and a network meta-analysis (NMA) was performed to compare outcomes of FFR-guided PCI, non-FFR-guided PCI, coronary artery bypass grafting (CABG), and medical treatment (MT) for AP based on estimated odds ratios (ORs). The study included 18,093 patients from 15 randomized controlled trials (RCTs). No evidence of inconsistency was observed among the studies. The NMA showed that the all-cause mortality of FFR-guided PCI was not significantly different from that of the other treatment groups (CABG: OR, 1.1; 95% confidence interval [CI], 0.67-1.7; non-FFR-guided PCI: OR, 0.85; 95% CI, 0.53-1.4; and MT: OR, 0.83; 95% CI, 0.52-1.3). The NMA for the composite of all-cause mortality and myocardial infarction, which included 15,454 patients from 12 RCTs, showed that FFR-guided PCI significantly reduced the composite outcome compared with non-FFR-guided PCI and MT (non-FFR-guided PCI: OR, 0.66; 95% CI, 0.46-0.95 and MT: OR, 0.66; 95% CI, 0.46-0.95). Although FFR-guided PCI for AP did not show significant prognostic improvement compared with non-FFR-guided PCI, CABG, and MT, FFR-guided PCI may significantly reduce the composite of all-cause mortality and myocardial infarction compared with non-FFR-guided PCI and MT.

Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning.

The development of deep learning technology has enabled machines to achieve high-level accuracy in interpreting medical images. While many previous studies have examined the detection of pulmonary nodules in chest X-rays using deep learning, the application of this technology to heart failure remains rare. In this paper, we investigated the performance of a deep learning algorithm in terms of diagnosing heart failure using images obtained from chest X-rays. We used 952 chest X-ray images from a labeled database published by the National Institutes of Health. Two cardiologists verified and relabeled a total of 260 "normal" and 378 "heart failure" images, with the remainder being discarded because they had been incorrectly labeled. Data augmentation and transfer learning were used to obtain an accuracy of 82% in diagnosing heart failure using the chest X-ray images. Furthermore, heatmap imaging allowed us to visualize decisions made by the machine. Deep learning can thus help support the diagnosis of heart failure using chest X-ray images.

Erratum: Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning.

An error appeared in the article entitled "Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning" by Takuya Matsumoto, Satoshi Kodera, Hiroki Shinohara, Hirotaka Ieki, Toshihiro Yamaguchi, Yasutomi Higashikuni, Arihiro Kiyosue, Kaoru Ito, Jiro Ando, Eiki Takimoto, Hiroshi Akazawa, Hiroyuki Morita, Issei Komuro (Vol. 61, No. 4, 781-786, 2020). The Figure 5on page 784 should be replaced by the following figure.

Noncompaction of the Ventricular Myocardium and Polycystic Kidney Disease: A Case Report.

Autosomal dominant polycystic kidney disease (ADPKD) is one of the most common hereditary disorders, characterized by the formation of multiple cysts in the kidneys and other organs, as well as noncystic manifestations such as cerebral aneurysm. The most common cardiovascular disorders associated with ADPKD include valvular abnormalities and aortic aneurysm. An association between ADPKD and impaired left ventricular function has occasionally been reported. We describe a 74-year-old woman with ADPKD and exertional dyspnea. Impaired left ventricular function resulting from noncompaction of the ventricular myocardium (NVM) and secondary left ventricular aneurysm were diagnosed. Cardiac sarcoidosis and ischemic heart disease were ruled out. Myocardial ischemia resulting from NVM was the presumptive cause of the ventricular aneurysm. To our knowledge, this is the first report of concurrent isolated NVM and left ventricular aneurysm in a patient with ADPKD. ADPKD and various cardiomyopathies, including NVM, are all reported to involve mutations of sarcomere genes, suggesting a possible link between the conditions.

Usefulness of contrast computed tomography to detect left ventricular apical thrombus associated with takotsubo cardiomyopathy.

Left ventricular (LV) apical thrombus can rarely occur during the early phase of takotsubo cardiomyopathy. We report such a case that was depicted clearly in contrast computed tomography (CT) but not in initial echocardiography. Because LV thrombus may lead to thromboembolic events, we should evaluate all patients with takotsubo cardiomyopathy for the presence of a LV thrombus. LV thrombus is generally recognized with echocardiography in the course of follow-up, but limited depiction of the LV apex with echocardiography can make evaluation of LV thrombus difficult. Contrast CT is useful to detect LV apical thrombus associated with takotsubo cardiomyopathy.

Acute Myocardial Infarction Caused by Thrombotic Microangiopathy Complicated With Myelodysplastic Syndrome.

Thrombotic microangiopathy (TMA) is a rare but lethal multisystem disease characterized by peripheral thrombocytopenia, microangiopathic hemolytic anemia, fever, and various stages of renal and neurological dysfunctions.(1,2)) The causes of TMA are mainly thrombotic thrombocytopenic purpura (TTP) or hemolytic-uremic syndrome (HUS), and cases of TMA related to myelodysplastic syndrome (MDS) are quite rare. Herein, we report a case of acute myocardial infarction (AMI) caused by TMA which is strongly suspected to have a relationship to MDS, and discuss the treatment of our patient who needed antiplatelet or anticoagulant therapy after AMI, while on the other hand, had pancytopenia and a bleeding event due to MDS.

Double Stranded RNA-Dependent Protein Kinase is Necessary for TNF-α-Induced Osteoclast Formation In Vitro and In Vivo.

Double-stranded RNA-dependent protein kinase (PKR) is involved in cell cycle progression, cell proliferation, cell differentiation, tumorgenesis, and apoptosis. We previously reported that PKR is required for differentiation and calcification in osteoblasts. TNF-α plays a key role in osteoclast differentiation. However, it is unknown about the roles of PKR in the TNF-α-induced osteoclast differentiation. The expression of PKR in osteoclast precursor RAW264.7 cells increased during TNF-α-induced osteoclastogenesis. The TNF-α-induced osteoclast differentiation in bone marrow-derived macrophages and RAW264.7 cells was markedly suppressed by the pretreatment of PKR inhibitor, 2-aminopurine (2AP), as well as gene silencing of PKR. The expression of gene markers in the differentiated osteoclasts including TRAP, Calcitonin receptor, cathepsin K, and ATP6V0d2 was also suppressed by the 2AP treatment. Bone resorption activity of TNF-α-induced osteoclasts was also supressed by 2AP treatment. Inhibition of PKR supressed the TNF-α-induced activation of NF-κB and MAPK in RAW264.7 cells. 2AP inhibited both the nuclear translocation of NF-κB and its transcriptional activity in RAW264.7 cells. 2AP inhibited the TNF-α-induced expression of NFATc1 and c-fos, master transcription factors in osteoclastogenesis. TNF-α-induced nuclear translocation of NFATc1 in mature osteoclasts was clearly inhibited by the 2AP treatment. The PKR inhibitor C16 decreased the TNF-α-induced osteoclast formation and bone resorption in mouse calvaria. The present study indicates that PKR is necessary for the TNF-α-induced osteoclast differentiation in vitro and in vivo.

The potential of the transformer-based survival analysis model, SurvTrace, for predicting recurrent cardiovascular events and stratifying high-risk patients with ischemic heart disease.

INTRODUCTION: Ischemic heart disease is a leading cause of death worldwide, and its importance is increasing with the aging population. The aim of this study was to evaluate the accuracy of SurvTrace, a survival analysis model using the Transformer-a state-of-the-art deep learning method-for predicting recurrent cardiovascular events and stratifying high-risk patients. The model's performance was compared to that of a conventional scoring system utilizing real-world data from cardiovascular patients. METHODS: This study consecutively enrolled patients who underwent percutaneous coronary intervention (PCI) at the Department of Cardiovascular Medicine, University of Tokyo Hospital, between 2005 and 2019. Each patient's initial PCI at our hospital was designated as the index procedure, and a composite of major adverse cardiovascular events (MACE) was monitored for up to two years post-index event. Data regarding patient background, clinical presentation, medical history, medications, and perioperative complications were collected to predict MACE. The performance of two models-a conventional scoring system proposed by Wilson et al. and the Transformer-based model SurvTrace-was evaluated using Harrell's c-index, Kaplan-Meier curves, and log-rank tests. RESULTS: A total of 3938 cases were included in the study, with 394 used as the test dataset and the remaining 3544 used for model training. SurvTrace exhibited a mean c-index of 0.72 (95% confidence intervals (CI): 0.69-0.76), which indicated higher prognostic accuracy compared with the conventional scoring system's 0.64 (95% CI: 0.64-0.64). Moreover, SurvTrace demonstrated superior risk stratification ability, effectively distinguishing between the high-risk group and other risk categories in terms of event occurrence. In contrast, the conventional system only showed a significant difference between the low-risk and high-risk groups. CONCLUSION: This study based on real-world cardiovascular patient data underscores the potential of the Transformer-based survival analysis model, SurvTrace, for predicting recurrent cardiovascular events and stratifying high-risk patients.

An explainable artificial intelligence-enabled electrocardiogram analysis model for the classification of reduced left ventricular function.

Aims: The black box nature of artificial intelligence (AI) hinders the development of interpretable AI models that are applicable in clinical practice. We aimed to develop an AI model for classifying patients of reduced left ventricular ejection fraction (LVEF) from 12-lead electrocardiograms (ECG) with the decision-interpretability. Methods and results: We acquired paired ECG and echocardiography datasets from the central and co-operative institutions. For the central institution dataset, a random forest model was trained to identify patients with reduced LVEF among 29 907 ECGs. Shapley additive explanations were applied to 7196 ECGs. To extract the model's decision criteria, the calculated Shapley additive explanations values were clustered for 192 non-paced rhythm patients in which reduced LVEF was predicted. Although the extracted criteria were different for each cluster, these criteria generally comprised a combination of six ECG findings: negative T-wave inversion in I/V5-6 leads, low voltage in I/II/V4-6 leads, Q wave in V3-6 leads, ventricular activation time prolongation in I/V5-6 leads, S-wave prolongation in V2-3 leads, and corrected QT interval prolongation. Similarly, for the co-operative institution dataset, the extracted criteria comprised a combination of the same six ECG findings. Furthermore, the accuracy of seven cardiologists' ECG readings improved significantly after watching a video explaining the interpretation of these criteria (before, 62.9% ± 3.9% vs. after, 73.9% ± 2.4%; P = 0.02). Conclusion: We visually interpreted the model's decision criteria to evaluate its validity, thereby developing a model that provided the decision-interpretability required for clinical application.

Achalasia with esophageal intramural hematoma treated by per-oral endoscopic myotomy (POEM).

Esophageal intramural hematoma (EIH) is a condition which occurs as a result of hemorrhage within the esophageal wall including the submucosal layer. However, reports of EIH on achalasia patients are quite limited and per-oral endoscopic myotomy (POEM) for achalasia with EIH has not been reported. This is the first case report that demonstrated a successful treatment of achalasia with EIH by POEM. In achalasia, since there is absence of lower esophageal sphincter relaxation, as barotraumatic pathogenesis, an increase in the intraesophageal pressure may cause EIH. As direct traumatic pathogenesis, the stasis of food may directly injure the esophageal wall resulting in EIH. After confirming the hematoma healed until it became an ulcer, POEM was performed on the posterior axis since the intramural hematoma was located anteriorly. The procedure was completed successfully without any occurrence of adverse events. On 2-months follow-up, improvement in dysphagia was noted, and complete epithelialization of the intramural hematoma region was seen on endoscopic examination. On 1-year follow-up, patient did not have recurrence of dysphagia and intramural hematoma. In summary, we reported a case of achalasia with EIH, which was treated by POEM. POEM procedure may be effective not only for the improvement of dysphagia but also for a better ulcer healing and prevention of intramural hematoma recurrence.