Identifying patients with acute aortic dissection using an electrocardiogram with convolutional neural network.

Background: The potential of utilizing artificial intelligence with electrocardiography (ECG) for initial screening of aortic dissection (AD) is promising. However, achieving a high positive predictive rate (PPR) remains challenging. Methods and results: This retrospective analysis of a single-center, prospective cohort study (Shinken Database 2010-2017, N = 19,170) used digital 12-lead ECGs from initial patient visits. We assessed a convolutional neural network (CNN) model's performance for AD detection with eight-lead (I, II, and V1-6), single-lead, and double-lead (I, II) ECGs via five-fold cross-validation. The mean age was 63.5 ± 12.5 years for the AD group (n = 147) and 58.1 ± 15.7 years for the non-AD group (n = 19,023). The CNN model achieved an area under the curve (AUC) of 0.936 (standard deviation [SD]: 0.023) for AD detection with eight-lead ECGs. In the entire cohort, the PPR was 7 %, with 126 out of 147 AD cases correctly diagnosed (sensitivity 86 %). When applied to patients with D-dimer levels ≥1 µg/dL and a history of hypertension, the PPR increased to 35 %, with 113 AD cases correctly identified (sensitivity 86 %). The single V1 lead displayed the highest diagnostic performance (AUC: 0.933, SD: 0.03), with PPR improvement from 8 % to 38 % within the same population. Conclusions: Our CNN model using ECG data for AD detection achieved an over 30% PPR when applied to patients with elevated D-dimer levels and hypertension history while maintaining sensitivity. A similar level of performance was observed with a single-lead V1 ECG in the CNN model.

Evaluating convolutional neural network-enhanced electrocardiography for hypertrophic cardiomyopathy detection in a specialized cardiovascular setting.

The efficacy of convolutional neural network (CNN)-enhanced electrocardiography (ECG) in detecting hypertrophic cardiomyopathy (HCM) and dilated HCM (dHCM) remains uncertain in real-world applications. This retrospective study analyzed data from 19,170 patients (including 140 HCM or dHCM) in the Shinken Database (2010-2017). We evaluated the sensitivity, positive predictive rate (PPR), and F1 score of CNN-enhanced ECG in a ''basic diagnosis'' model (total disease label) and a ''comprehensive diagnosis'' model (including disease subtypes). Using all-lead ECG in the "basic diagnosis" model, we observed a sensitivity of 76%, PPR of 2.9%, and F1 score of 0.056. These metrics improved in cases with a diagnostic probability of ≥ 0.9 and left ventricular hypertrophy (LVH) on ECG: 100% sensitivity, 8.6% PPR, and 0.158 F1 score. The ''comprehensive diagnosis'' model further enhanced these figures to 100%, 13.0%, and 0.230, respectively. Performance was broadly consistent across CNN models using different lead configurations, particularly when including leads viewing the lateral walls. While the precision of CNN models in detecting HCM or dHCM in real-world settings is initially low, it improves by targeting specific patient groups and integrating disease subtype models. The use of ECGs with fewer leads, especially those involving the lateral walls, appears comparably effective.

Lead-Specific Performance for Atrial Fibrillation Detection in Convolutional Neural Network Models Using Sinus Rhythm Electrocardiography.

Background: We developed a convolutional neural network (CNN) model to detect atrial fibrillation (AF) using the sinus rhythm ECG (SR-ECG). However, the diagnostic performance of the CNN model based on different ECG leads remains unclear. Methods and Results: In this retrospective analysis of a single-center, prospective cohort study, we identified 616 AF cases and 3,412 SR cases for the modeling dataset among new patients (n=19,170). The modeling dataset included SR-ECGs obtained within 31 days from AF-ECGs in AF cases and SR cases with follow-up ≥1,095 days. We evaluated the CNN model's performance for AF detection using 8-lead (I, II, and V1-6), single-lead, and double-lead ECGs through 5-fold cross-validation. The CNN model achieved an area under the curve (AUC) of 0.872 (95% confidence interval (CI): 0.856-0.888) and an odds ratio of 15.24 (95% CI: 12.42-18.72) for AF detection using the eight-lead ECG. Among the single-lead and double-lead ECGs, the double-lead ECG using leads I and V1 yielded an AUC of 0.871 (95% CI: 0.856-0.886) with an odds ratio of 14.34 (95% CI: 11.64-17.67). Conclusions: We assessed the performance of a CNN model for detecting AF using eight-lead, single-lead, and double-lead SR-ECGs. The model's performance with a double-lead (I, V1) ECG was comparable to that of the 8-lead ECG, suggesting its potential as an alternative for AF screening using SR-ECG.

Identification of patients with dilated phase of hypertrophic cardiomyopathy using a convolutional neural network applied to multiple, dual, and single lead electrocardiograms.

Background: This study sought to develop an artificial intelligence-derived model to detect the dilated phase of hypertrophic cardiomyopathy (dHCM) on digital electrocardiography (ECG) and to evaluate the performance of the model applied to multiple-lead or single-lead ECG. Methods: This is a retrospective analysis using a single-center prospective cohort study (Shinken Database 2010-2017, n = 19,170). After excluding those without a normal P wave on index ECG (n = 1,831) and adding dHCM patients registered before 2009 (n = 39), 17,378 digital ECGs were used. Totally 54 dHCM patients were identified of which 11 diagnosed at baseline, 4 developed during the time course, and 39 registered before 2009. The performance of the convolutional neural network (CNN) model for detecting dHCM was evaluated using eight-lead (I, II, and V1-6), single-lead, and double-lead (I, II) ECGs with the five-fold cross validation method. Results: The area under the curve (AUC) of the CNN model to detect dHCM (n = 54) with eight-lead ECG was 0.929 (standard deviation [SD]: 0.025) and the odds ratio was 38.64 (SD 9.10). Among the single-lead and double-lead ECGs, the AUC was highest with the single lead of V5 (0.953 [SD: 0.038]), with an odds ratio of 58.89 (SD:68.56). Conclusion: Compared with the performance of eight-lead ECG, the most similar performance was achieved with the model with a single V5 lead, suggesting that this single-lead ECG can be an alternative to eight-lead ECG for the screening of dHCM.

Cardiovascular events and artificial intelligence-predicted age using 12-lead electrocardiograms.

Background: There is increasing evidence that 12-lead electrocardiograms (ECG) can be used to predict biological age, which is associated with cardiovascular events. However, the utility of artificial intelligence (AI)-predicted age using ECGs remains unclear. Methods: Using a single-center database, we developed an AI-enabled ECG using 17 042 sinus rhythm ECGs (SR-ECG) to predict chronological age (CA) with a convolutional neural network that yields AI-predicted age. Using the 5-fold cross validation method, AI-predicted age deriving from the test dataset was yielded for all ECGs. The incidence by AgeDiff and the areas under the curve by receiver operating characteristic curve with AI-predicted age for cardiovascular events were analyzed. Results: During the mean follow-up period of 460.1 days, there were 543 cardiovascular events. The annualized incidence of cardiovascular events was 2.24 %, 2.44 %, and 3.01 %/year for patients with AgeDiff < -6, -6 to ≤6, and >6 years, respectively. The areas under the curve for cardiovascular events with CA and AI-predicted age, respectively, were 0.673 and 0.679 (Delong's test, P = 0.388) for all patients; 0.642 and 0.700 (P = 0.003) for younger patients (CA < 60 years); and 0.584 and 0.570 (P = 0.268) for older patients (CA ≥ 60 years). Conclusions: AI-predicted age using 12-lead ECGs showed superiority in predicting cardiovascular events compared with CA in younger patients, but not in older patients.

Identifying patients with atrial fibrillation during sinus rhythm on ECG: Significance of the labeling in the artificial intelligence algorithm.

BACKGROUND: This study aimed to increase the knowledge on how to enhance the performance of artificial intelligence (AI)-enabled electrocardiography (ECG) to detect atrial fibrillation (AF) on sinus rhythm ECG (SR-ECG). METHODS: It is a retrospective analysis of a single-center, prospective cohort study (Shinken Database). We developed AI-enabled ECG using SR-ECG to predict AF with a convolutional neural network (CNN). Among new patients in our hospital (n = 19,170), 276 AF label (having ECG on AF [AF-ECG] in the ECG database) and 1896 SR label with following three conditions were identified in the derivation dataset: (1) without structural heart disease, (2) in AF label, SR-ECG was taken within 31 days from AF-ECG, and (3) in SR label, follow-up ≥ 1,095 days. Three patterns of AF label were analyzed by timing of SR-ECG to AF-ECG (before/after/before-or-after, CNN algorithm 1 to 3). The outcome measurement was area under the curve (AUC), sensitivity, specificity, accuracy, and F1 score. As an extra-testing dataset, the performance of AI-enabled ECG was tested in patients with structural heart disease. RESULTS: The AUC of AI-enabled ECG with CNN algorithm 1, 2, and 3 in the derivation dataset was 0.83, 0.88, and 0.86, respectively; when tested in patients with structural heart disease, 0.75, 0.81, and 0.78, respectively. CONCLUSION: We confirmed high performance of AI-enabled ECG to detect AF on SR-ECG in patients without structural heart disease. The performance enhanced especially when SR-ECG after index AF-ECG was included in the algorithm, which was consistent in patients with structural heart disease.

Influence of coil current configuration in magnetic stimulation of a nerve fiber in inhomogeneous and anisotropic conducting media.

In this study, we used a computer simulation to investigate the effects of the coil current waveform and direction on the excitation processes of the nerve axon in inhomogeneous and anisotropic conducting media in magnetic stimulation. We assumed that the nerve axon was located in the media with 2 regions having different conductivities or electrical anisotropy that simulate different tissue types. The distribution of induced electric fields was calculated with the finite element method (FEM). The nerve fiber was modeled after equivalent electrical circuits having active nodes of Ranvier. The direction of the coil current at the intersection of a figure-eight coil was assumed to flow perpendicular to the nerve axon. We observed the excitation threshold when the coil current waveform and direction are changed with varying the electrical properties such as tissue electrical conductivity and anisotropy. The simulation results show that the threshold decreases with the increase of conductivity ratio between 2 regions and it also depends on the coil current waveform and direction. Biphasic coil current has lower threshold than monophasic one when the current direction is the same in both waveforms. The results also suggest that the tissue anisotropy strongly affects the excitation threshold. The threshold increases with the increase of tissue anisotropic ratio of longitudinal direction to the transverse one respect to the nerve axon. The results in this study give useful information to explain the experimental results of the magnetic stimulation of human peripheral nervous systems and the theoretical model is applicable to understand the characteristics in magnetic stimulation of both peripheral and central nervous systems.

[Acupuncture treatment for a patient with prolonged consciousness disturbance after a brain tumor surgery].

Acupuncture has long been applied as a therapeutic technique in China, Japan, Korea and other countries. Recently, its application began to be extended to the treatment of neural disorders. We experienced a 13-year-old boy with prolonged consciousness disturbance after a pineal tumor surgery and muscle contracture of lower extremity by long-term recumbency. We applied him acupuncture treatment for 4 months which was effective to alleviate these symptoms. Repeated IMP SPECT showed improvement of the cerebral blood flow (CBF) during the course of acupuncture therapy. Acupuncture was effective to improve prolonged coma after a brain surgery and also muscle contracture by long-lasting recumbency. CBF showed a slight increase along with the recovery of consciousness suggesting a strong relevance between CBF and improvement of these symptoms.

Reduction in the motor unit number estimate (MUNE) after cerebral infarction.

We examined the relationship between the degree to which motor unit number estimates (MUNEs) decrease in association with the clinical features of patients with the infarction. Using a multiple-point stimulation technique, we obtained the MUNE of the hypothenar muscle group in 13 age-matched control subjects and 30 patients with cerebral infarction. In all patients, we obtained the Japan Stroke Scale (JSS) and head MR images. In 8 patients with acute cerebral infarction, admitted within 24 h after onset, we also obtained head MR angiograms and single-photon emission CT. There was a decrease in the MUNE of the hypothenar muscle group on the affected side of 24 patients with cerebral infarction and hand weakness. The decrease in the MUNE started from 4 to 30 h after the infarction, when T1-weighted MR images of the brain involved were normal. The degree to which the MUNE decreased correlated with the part of the JSS showing the upper extremity weakness. A decrease in the MUNE of the hypothenar muscle group within 30 h after cerebral infarction may be due to transsynaptic inhibition of the spinal alpha motor neurons innervating this muscle.

The numeric calculation of eddy current distributions in transcranial magnetic stimulation.

Transcranial magnetic stimulation (TMS) is a method to stimulate neurons in the brain. It is necessary to obtain eddy current distributions and determine parameters such as position, radius and bend-angle of the coil to stimulate target area exactly. In this study, we performed FEM-based numerical simulations of eddy current induced by TMS using three-dimentional human head model with inhomogeneous conductivity. We used double-cone coil and changed the coil radius and bend-angle of coil. The result of computer simulation showed that as coil radius increases, the eddy current became stronger everywhere. And coil with bend-angle of 22.5 degrees induced stronger eddy current than the coil with bendangle of 0 degrees. Meanwhile, when the bend-angle was 45 degrees, eddy current became weaker than these two cases. This simulation allowed us to determine appropriate parameter easier.

[Acupuncture treatment for a patient with diffuse axonal injury--report no. 2].

Acupuncture has been applied as a therapeutic technique in China, Japan and East Asia. Recently, its application is extended to treat neural injuries. We describe a 26-year-old man with consciousness disturbance and intense muscle spasticity of extremities due to severe diffuse axonal injury (DAI) in whom acupuncture treatment for 1 month was effective to alleviate these symptoms remarkably. We also investigated the cerebral blood flow two times by 123I-IMP SPECT in acupuncture period. Acupuncture treatment may be effective to improve consciousness disturbance and intense spasticity of DAI and to modulate cerebral blood flow.

[Acupuncture treatment for a patient with diffuse axonal injury].

Acupuncture has been used as a therapeutic technique in China, Japan and East Asia. Recently, it is used to treat neural injuries. We describe a 6-year-old boy with consciousness disturbance and heavy muscle spasticity of extremities due to severe diffuse axonal injury (DAI) in whom acupuncture treatment for 6 months alleviated these symptoms remarkably. Acupuncture treatment may be effective to improve consciousness disturbance and heavy spasticity of DAI.

Reduction in the motor unit number estimate (MUNE) after cerebral infarction.

BACKGROUND: The mechanism of the decrease in motor unit number estimates (MUNEs) after cerebral infarction has not been studied systematically. We examined the relationship between the degree to which MUNEs decreased and the other clinical features of patients with the infarction. METHODS: Using a multiple point stimulation technique, we obtained the MUNE of the hypothenar muscle group in 13 age-matched control subjects and 30 patients with cerebral infarction. In all patients, we obtained the Japan Stroke Scale (JSS) and head MR images. In eight patients with acute cerebral infarction, admitted within 24 h after onset, we also obtained head MR angiograms and single-photon emission CT. FINDINGS: There was a decrease in the MUNE of the hypothenar muscle group on the affected side of 24 patients with cerebral infarction and hand weakness. The decrease in the MUNE started from 4 to 30 h after the infarction, when T1-weighted MR images of the brain involved were normal. The degree to which the MUNE decreased correlated with the part of the JSS showing the upper extremity weakness. INTERPRETATIONS: A decrease in the MUNE of the hypothenar muscle group within 30 h after cerebral infarction may be due to trans-synaptic inhibition of the spinal alpha motor neurons innervating this muscle.