Diagnostic accuracy of artificial intelligence in detecting left ventricular hypertrophy by electrocardiograph: a systematic review and meta-analysis

Our research aimed to evaluate the diagnostic accuracy of AI in detecting LVH with electrocardiography and evaluate it to the traditional standards, together with Cornell’s and Sokolow–Lyon’s standards. Our findings recommend that, by SROC, AI was related to greater diagnostic accuracy as in comparison with the opposite two standard standards’s. Additional, we noticed a notable improve in sensitivity for LVH detection by AI, when in comparison with Sokolow–Lyon’s and Cornell’s standards. Nonetheless, the specificity of AI was comparatively decrease than that of the traditional standards. On account of its enhanced sensitivity, AI could possibly be used as a screening software in conjunction with standard standards to determine LVH.

To enhance diagnostic efficiency in ECG detection of LVH, a number of ECG standards have been iteratively refined over a long time20. For example, Peguero et al. proposed a novel ECG criterion that outperformed Cornell’s voltage standards on sensitivity, 62% over 35%, respectively19. Conversely, the earlier research specializing in sufferers over the age of 65 discovered Cornell’s Product standards with improved efficiency, an AUC of 0.62, albeit yielding suboptimal outcomes21. Based on these pre-existing publications, the first limitations of standard standards have been recognized as a disparity between sensitivity and specificity, in addition to the exclusion of ECG abnormalities that bear prognostic significance3,22,23,24. To deal with these limitations, machine studying and deep learning-based AI methods have been employed, enabling the utilization of in depth ECG-LVH information and extremely relevant ECG options. The flexibility of AI algorithms to include various varieties of enter information, together with photographs and waveforms, has confirmed to be essential. For instance, Kwon et al. integrated not solely variables such because the presence of atrial fibrillation or flutter, QT interval, QTc, QRS length, R-wave axis, and T-wave axis as enter information but additionally uncooked ECG information in a two-dimensional numeric format9.

Our research incorporates a number of machine studying strategies which were beforehand developed and employed in related analysis. For example, Sparapani et al.13 devised the BART-LVH standards for detecting LVH by leveraging BART, a machine-learning approach. They utilized affected person traits resembling demographics, biometrics, and heart problems threat components like blood stress and physique mass index. Moreover, De la Garza-Salazar et al.11 employed logistic regression for information dimensionality discount and subsequently constructed a resolution tree mannequin utilizing the C5.0 algorithm. This resolution tree mannequin integrated a number of ECG measurements, together with ST abnormalities, S wave voltage in lead V4, intrinsicoid deflection in lead V6 (qR length ≥ 0.05 s), detrimental deflection of P wave in lead V1, and R wave voltage in lead aVR. This strategy holds promise for real-life purposes as a result of its simplicity and utilization of fundamental parameters measured by ECG machines.

One other profitable instance of a non-black field mannequin in diagnosing echo-LVH, demonstrated by De la Garza-Salazar et al., is the Cardiac Hypertrophy Pc-based Mannequin (CHCM). This AI mannequin achieved balanced sensitivity and specificity, surpassing the accuracy of conventional standards like Cornell and Sokolow–Lyon. By integrating various varieties of enter information, together with ECG quantitative information and affected person traits, AI algorithms provide a promising avenue for enhancing LVH detection accuracy25.

The utilization of AI and black field fashions for diagnosing LVH holds promise for advancing ECG analyses. Nonetheless, a notable downside of AI and machine studying is their lack of transparency concerning the reasoning behind their diagnoses, doubtlessly resulting in the loss of prognostic markers. For example, whereas the pressure sample in ECG is acknowledged as an necessary marker of LVH, it additionally serves as a prognostic indicator in varied medical situations, as demonstrated in research such because the Framingham Coronary heart Research and quite a few cohorts26,27,28.

To strike a steadiness between diagnostic accuracy and medical significance, one strategy includes harnessing non-black field AI fashions to extract and analyze a broader vary of ECG parameters. By embracing interpretable AI methods, researchers can uncover insights into the relationships between ECG options and the prognosis of LVH, thus guaranteeing a extra complete understanding of the diagnostic course of and its implications for affected person care.

Research limitations

There are a few limitations in our meta-analysis. First, majorities of the included research have been observational. Due to this fact, residual confounders weren’t utterly excluded, deleteriously complicating the outcomes. The utilization of AI in diagnosing situations might result in each overestimation and underestimation of its accuracy. Second, the heterogeneity of this research was important because of the inclusion of research that featured varied research designs together with varieties of AI strategies, demographic information, people’ underlying ailments, and different components that might not be decided. Therefore, the interpretation of this evaluation should be cautiously utilized with the suitable and relevant contexts. Lastly, our research didn’t intention to particularly assess the accuracy of the LVH detection algorithms. As an alternative, our major goal was to supply an outline of the general validity of the newly developed LVH utilizing AI.

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