The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
La dénervation rénale par cathéter (RDN) est un nouveau traitement pour réduire la pression artérielle chez les patients souffrant d'hypertension résistante à l'aide d'un cathéter à base d'énergie, principalement un courant de radiofréquence (RF), en éliminant le nerf sympathique rénal. Cependant, plusieurs traitements RDN incohérents ont été signalés, principalement en raison de la zone de chauffage étroite du courant RF et de l'incapacité de confirmer une ablation nerveuse réussie dans une zone profonde. Nous avons proposé l’énergie micro-ondes comme alternative pour créer une zone d’ablation plus large. Cependant, confirmer une ablation réussie reste un problème. Dans cet article, nous avons conçu une méthode de prédiction pour les sites d’ablation des nerfs rénaux profonds en utilisant l’apprentissage automatique (ML) hybride basé sur le calcul numérique en combinaison avec un cathéter à micro-ondes. Ce travail constitue une première étape visant à vérifier la capacité de prédiction hybride du ML dans une situation réelle. Un cathéter avec une antenne coaxiale à fente unique à 2.45 GHz avec un cathéter à ballonnet, combiné à une fine sonde thermomètre sur la surface du ballon, est proposé. La température de la lumière mesurée par la sonde est utilisée comme entrée ML pour prédire l’augmentation de la température sur le site d’ablation. Des expériences de chauffage utilisant des fantômes de trous de 6 et 8 mm avec une puissance excitée de 41.3 W et des fantômes de 8 mm avec une puissance excitée de 36.4 W ont été réalisées huit fois chacune pour vérifier la faisabilité et la précision de l'algorithme ML. De plus, la température sur le site d'ablation est mesurée à titre de référence. L'algorithme de prédiction par ML est en bon accord avec la référence, avec une différence maximale de 6°C et 3°C en 6 et 8 mm (les deux puissances), respectivement. Dans l’ensemble, l’algorithme ML proposé est capable de prédire l’augmentation de la température du site d’ablation avec une grande précision.
Aditya RAKHMADI
Chiba University
Kazuyuki SAITO
Chiba University
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Aditya RAKHMADI, Kazuyuki SAITO, "Feasibility Study of Numerical Calculation and Machine Learning Hybrid Approach for Renal Denervation Temperature Prediction" in IEICE TRANSACTIONS on Electronics,
vol. E106-C, no. 12, pp. 799-807, December 2023, doi: 10.1587/transele.2023ECP5002.
Abstract: Transcatheter renal denervation (RDN) is a novel treatment to reduce blood pressure in patients with resistant hypertension using an energy-based catheter, mostly radio frequency (RF) current, by eliminating renal sympathetic nerve. However, several inconsistent RDN treatments were reported, mainly due to RF current narrow heating area, and the inability to confirm a successful nerve ablation in a deep area. We proposed microwave energy as an alternative for creating a wider ablation area. However, confirming a successful ablation is still a problem. In this paper, we designed a prediction method for deep renal nerve ablation sites using hybrid numerical calculation-driven machine learning (ML) in combination with a microwave catheter. This work is a first-step investigation to check the hybrid ML prediction capability in a real-world situation. A catheter with a single-slot coaxial antenna at 2.45 GHz with a balloon catheter, combined with a thin thermometer probe on the balloon surface, is proposed. Lumen temperature measured by the probe is used as an ML input to predict the temperature rise at the ablation site. Heating experiments using 6 and 8 mm hole phantom with a 41.3 W excited power, and 8 mm with 36.4 W excited power, were done eight times each to check the feasibility and accuracy of the ML algorithm. In addition, the temperature on the ablation site is measured for reference. Prediction by ML algorithm agrees well with the reference, with a maximum difference of 6°C and 3°C in 6 and 8 mm (both power), respectively. Overall, the proposed ML algorithm is capable of predicting the ablation site temperature rise with high accuracy.
URL: https://global.ieice.org/en_transactions/electronics/10.1587/transele.2023ECP5002/_p
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@ARTICLE{e106-c_12_799,
author={Aditya RAKHMADI, Kazuyuki SAITO, },
journal={IEICE TRANSACTIONS on Electronics},
title={Feasibility Study of Numerical Calculation and Machine Learning Hybrid Approach for Renal Denervation Temperature Prediction},
year={2023},
volume={E106-C},
number={12},
pages={799-807},
abstract={Transcatheter renal denervation (RDN) is a novel treatment to reduce blood pressure in patients with resistant hypertension using an energy-based catheter, mostly radio frequency (RF) current, by eliminating renal sympathetic nerve. However, several inconsistent RDN treatments were reported, mainly due to RF current narrow heating area, and the inability to confirm a successful nerve ablation in a deep area. We proposed microwave energy as an alternative for creating a wider ablation area. However, confirming a successful ablation is still a problem. In this paper, we designed a prediction method for deep renal nerve ablation sites using hybrid numerical calculation-driven machine learning (ML) in combination with a microwave catheter. This work is a first-step investigation to check the hybrid ML prediction capability in a real-world situation. A catheter with a single-slot coaxial antenna at 2.45 GHz with a balloon catheter, combined with a thin thermometer probe on the balloon surface, is proposed. Lumen temperature measured by the probe is used as an ML input to predict the temperature rise at the ablation site. Heating experiments using 6 and 8 mm hole phantom with a 41.3 W excited power, and 8 mm with 36.4 W excited power, were done eight times each to check the feasibility and accuracy of the ML algorithm. In addition, the temperature on the ablation site is measured for reference. Prediction by ML algorithm agrees well with the reference, with a maximum difference of 6°C and 3°C in 6 and 8 mm (both power), respectively. Overall, the proposed ML algorithm is capable of predicting the ablation site temperature rise with high accuracy.},
keywords={},
doi={10.1587/transele.2023ECP5002},
ISSN={1745-1353},
month={December},}
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TY - JOUR
TI - Feasibility Study of Numerical Calculation and Machine Learning Hybrid Approach for Renal Denervation Temperature Prediction
T2 - IEICE TRANSACTIONS on Electronics
SP - 799
EP - 807
AU - Aditya RAKHMADI
AU - Kazuyuki SAITO
PY - 2023
DO - 10.1587/transele.2023ECP5002
JO - IEICE TRANSACTIONS on Electronics
SN - 1745-1353
VL - E106-C
IS - 12
JA - IEICE TRANSACTIONS on Electronics
Y1 - December 2023
AB - Transcatheter renal denervation (RDN) is a novel treatment to reduce blood pressure in patients with resistant hypertension using an energy-based catheter, mostly radio frequency (RF) current, by eliminating renal sympathetic nerve. However, several inconsistent RDN treatments were reported, mainly due to RF current narrow heating area, and the inability to confirm a successful nerve ablation in a deep area. We proposed microwave energy as an alternative for creating a wider ablation area. However, confirming a successful ablation is still a problem. In this paper, we designed a prediction method for deep renal nerve ablation sites using hybrid numerical calculation-driven machine learning (ML) in combination with a microwave catheter. This work is a first-step investigation to check the hybrid ML prediction capability in a real-world situation. A catheter with a single-slot coaxial antenna at 2.45 GHz with a balloon catheter, combined with a thin thermometer probe on the balloon surface, is proposed. Lumen temperature measured by the probe is used as an ML input to predict the temperature rise at the ablation site. Heating experiments using 6 and 8 mm hole phantom with a 41.3 W excited power, and 8 mm with 36.4 W excited power, were done eight times each to check the feasibility and accuracy of the ML algorithm. In addition, the temperature on the ablation site is measured for reference. Prediction by ML algorithm agrees well with the reference, with a maximum difference of 6°C and 3°C in 6 and 8 mm (both power), respectively. Overall, the proposed ML algorithm is capable of predicting the ablation site temperature rise with high accuracy.
ER -