AI diagnosis of heart sounds differentiated with super StethoScope

Ogawa, Shimpei; Namino, Fuminori; Mori, Tomoyo; Sato, Ginga; Yamakawa, Toshitaka; Saito, Shumpei

doi:10.1016/j.jjcc.2023.09.007

Cited by 4 publications

(1 citation statement)

References 40 publications

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“…Munculnya kebutuhan petugas kesehatan terhadap keakuratan pendengaran dan pemeriksaan auskultasi jantung menghasilkan inovasi baru dalam bidang stetoskop elektronik (Li et al, 2021). Hal ini dilakukan untuk menutupi kekurangan stetoskop konvensional yaitu berkurangnya kualitas ampilifkasi selama auskultasi yang disebabkan karena berbagai hal misalnya kebisingan dan juga keberhasilan mendeteksi bunyi jantung yang bergantung oleh pengalaman praktisi (Ogawa et al, 2023). Stetoskop elektronik memiliki beberapa keunggulan lain dibandingkan stetoskop akustik, termasuk penyesuaian volume, peredam bising, diagnosis otomatis, auskultasi jarak jauh, gerakan tidak terbebani, dan lebih sedikit ketergantungan pada monitor telinga (Rennoll et al, 2021).…”

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Penggunaan Stetoskop Elektronik pada Kelainan Katup Jantung

Mangole,

Nurachmah,

Waluyo

et al. 2023

JOTING

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This study aims to see the effectiveness of using an electronic stethoscope compared to a conventional one in patients with heart valve disorders. The method used in this writing is a systematic review through several databases, namely Pubmed, ClinicalKey, Proquest, Science Direct, and Scopus. The article analysis process uses the PRISMA method. The research results show that electronic stethoscopes are more expensive because many parts and applications/software are added. Still, they are very efficient in accurately diagnosing specific heart sound abnormalities and have more advantages than conventional stethoscopes. In conclusion, electronic stethoscopes are very effective and efficient in increasing the speed and accuracy of diagnosing specific heart sound abnormalities, so they have more benefits. Keywords: Heart Valves, Electronic Stethoscope

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unclassified

Penggunaan Stetoskop Elektronik pada Kelainan Katup Jantung

Mangole,

Nurachmah,

Waluyo

et al. 2023

JOTING

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Prediction of Left Ventricle Pressure Indices Via a Machine Learning Approach Combining ECG, Pulse Oximetry, and Cardiac Sounds: a Preclinical Feasibility Study

Fassina,

Muzio,

Berboth

et al. 2024

J. Cardiovasc. Transl. Res.

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Heart failure (HF) is defined as the inability of the heart to meet body oxygen demand requiring an elevation in left ventricular filling pressures (LVP) to compensate. LVP increase can be assessed in the cardiac catheterization laboratory, but this procedure is invasive and time-consuming to the extent that physicians rather rely on non-invasive diagnostic tools. In this work, we assess the feasibility to develop a novel machine-learning (ML) approach to predict clinically relevant LVP indices. Synchronized invasive (pressure–volume tracings) and non-invasive signals (ECG, pulse oximetry, and cardiac sounds) were collected from anesthetized, closed-chest Göttingen minipigs. Animals were either healthy or had HF with reduced ejection fraction and circa 500 heartbeats were included in the analysis for each animal. The ML algorithm showed excellent prediction of LVP indices estimating, for instance, the end-diastolic pressure with a R2 of 0.955. This novel ML algorithm could assist clinicians in the care of HF patients. Graphical Abstract

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