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Ibrahim B and Jafari R. Cuffless Blood Pressure Monitoring from a Wristband with Calibration-Free Algorithms for Sensing Location Based on Bio-Impedance Sensor Array and Autoencoder. Sci Rep 2022 Jan; 12:319. DOI: 10.1038/s41598-021-03612-1IbrahimB and JafariR.Cuffless Blood Pressure Monitoring from a Wristband with Calibration-Free Algorithms for Sensing Location Based on Bio-Impedance Sensor Array and Autoencoder.2022Jan;12:319. DOI:10.1038/s41598-021-03612-1Open DOISearch in Google Scholar
Sel K, Osman D, Huerta N, Edgar A, Pettigrew RI and Jafari R. Continuous Cuffless Blood Pressure Monitoring with a Wearable Ring Bioimpedance Device. npj Digit. Med. 2023 Mar; 6:1–11. DOI: 10.1038/s41746-023-00796-wSelKOsmanDHuertaNEdgarAPettigrewRI and JafariR.Continuous Cuffless Blood Pressure Monitoring with a Wearable Ring Bioimpedance Device..2023Mar;6:1–11. DOI:10.1038/s41746-023-00796-wOpen DOISearch in Google Scholar
Kireev D, Sel K, Ibrahim B, Kumar N, Akbari A, Jafari R and Akinwande D. Continuous Cuffless Monitoring of Arterial Blood Pressure via Graphene Bioimpedance Tattoos. Nat. Nanotechnol. 2022 Aug; 17:864–70. DOI: 10.1038/s41565-022-01145-wKireevDSelKIbrahimBKumarNAkbariAJafariR and AkinwandeD.Continuous Cuffless Monitoring of Arterial Blood Pressure via Graphene Bioimpedance Tattoos..2022Aug;17:864–70. DOI:10.1038/s41565-022-01145-wOpen DOISearch in Google Scholar
Nematollahi MA, Askarinejad A, Asadollahi A, Bazrafshan M, Sarejloo S, Moghadami M, Sasannia S, Farjam M, Homayounfar R, Pezeshki B, Amini M, Roshanzamir M, Alizadehsani R, Bazrafshan H, Bazrafshan drissi H, Tan RS, Acharya UR and Islam MSS. A Cohort Study on the Predictive Capability of Body Composition for Diabetes Mellitus Using Machine Learning. J Diabetes Metab Disord 2023 Nov. DOI: 10.1007/s40200-023-01350-xNematollahiMAAskarinejadAAsadollahiABazrafshanMSarejlooSMoghadamiMSasanniaSFarjamMHomayounfarRPezeshkiBAminiMRoshanzamirMAlizadehsaniRBazrafshanHBazrafshan drissiHTanRSAcharyaUR and IslamMSS.A Cohort Study on the Predictive Capability of Body Composition for Diabetes Mellitus Using Machine Learning.2023Nov. DOI:10.1007/s40200-023-01350-xOpen DOISearch in Google Scholar
Sanai F, Sahid AS, Huvanandana J, Spoa S, Boyle LH, Hribar J, Wang DTY, Kwan B, Colagiuri S, Cox SJ and Telfer TJ. Evaluation of a Continuous Blood Glucose Monitor: A Novel and Non-Invasive Wearable Using Bioimpedance Technology. J Diabetes Sci Technol 2023 Mar; 17:336–44. DOI: 10.1177/19322968211054110SanaiFSahidASHuvanandanaJSpoaSBoyleLHHribarJWangDTYKwanBColagiuriSCoxSJ and TelferTJ.Evaluation of a Continuous Blood Glucose Monitor: A Novel and Non-Invasive Wearable Using Bioimpedance Technology.2023Mar;17:336–44. DOI:10.1177/19322968211054110Open DOISearch in Google Scholar
Pandeya SR, Nagy JA, Riveros D, Semple C, Taylor RS, Hu A, Sanchez B and Rutkove SB. Using Machine Learning Algorithms to Enhance the Diagnostic Performance of Electrical Impedance Myography. Muscle & Nerve 2022; 66:354–61. DOI: 10.1002/mus.27664PandeyaSRNagyJARiverosDSempleCTaylorRSHuASanchezB and RutkoveSB.Using Machine Learning Algorithms to Enhance the Diagnostic Performance of Electrical Impedance Myography.2022;66:354–61. DOI:10.1002/mus.27664Open DOISearch in Google Scholar
Schaeffer J, Gasper P, Garcia-Tamayo E, Gasper R, Adachi M, Gaviria-Cardona JP, Montoya-Bedoya S, Bhutani A, Schiek A, Goodall R, Findeisen R, Braatz RD and Engelke S. Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra. J. Electrochem. Soc. 2023 Jun; 170:060512. DOI: 10.1149/1945-7111/acd8fbSchaefferJGasperPGarcia-TamayoEGasperRAdachiMGaviria-CardonaJPMontoya-BedoyaSBhutaniASchiekAGoodallRFindeisenRBraatzRD and EngelkeS.Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra..2023Jun;170:060512. DOI:10.1149/1945-7111/acd8fbOpen DOISearch in Google Scholar
Chen X, Wang Z, Zhang X, Fu R, Wang D, Zhang M and Wang H. Deep Autoencoder Imaging Method for Electrical Impedance Tomography. IEEE Transactions on Instrumentation and Measurement 2021; 70:1–15. DOI: 10.1109/TIM.2021.3094834ChenXWangZZhangXFuRWangDZhangM and WangH.Deep Autoencoder Imaging Method for Electrical Impedance Tomography.2021;70:1–15. DOI:10.1109/TIM.2021.3094834Open DOISearch in Google Scholar
Wu Y, Chen B, Liu K, Zhu C, Pan H, Jia J, Wu H and Yao J. Shape Reconstruction With Multiphase Conductivity for Electrical Impedance Tomography Using Improved Convolutional Neural Network Method. IEEE Sensors Journal 2021 Apr; 21:9277–87. DOI: 10.1109/JSEN.2021.3050845WuYChenBLiuKZhuCPanHJiaJWuH and YaoJ.Shape Reconstruction With Multiphase Conductivity for Electrical Impedance Tomography Using Improved Convolutional Neural Network Method.2021Apr;21:9277–87. DOI:10.1109/JSEN.2021.3050845Open DOISearch in Google Scholar
Ren S, Guan R, Liang G and Dong F. RCRC: A Deep Neural Network for Dynamic Image Reconstruction of Electrical Impedance Tomography. IEEE Transactions on Instrumentation and Measurement 2021; 70:1–11. DOI: 10.1109/TIM.2021.3092061RenSGuanRLiangG and DongF.RCRC: A Deep Neural Network for Dynamic Image Reconstruction of Electrical Impedance Tomography.2021;70:1–11. DOI:10.1109/TIM.2021.3092061Open DOISearch in Google Scholar
Ren S, Sun K, Tan C and Dong F. A Two-Stage Deep Learning Method for Robust Shape Reconstruction With Electrical Impedance Tomography. IEEE Transactions on Instrumentation and Measurement 2020 Jul; 69:4887–97. DOI: 10.1109/TIM.2019.2954722RenSSunKTanC and DongF.A Two-Stage Deep Learning Method for Robust Shape Reconstruction With Electrical Impedance Tomography.2020Jul;69:4887–97. DOI:10.1109/TIM.2019.2954722Open DOISearch in Google Scholar
Liu D, Wang J, Shan Q, Smyl D, Deng J and Du J. DeepEIT: Deep Image Prior Enabled Electrical Impedance Tomography. IEEE Transactions on Pattern Analysis and Machine Intelligence 2023 Aug; 45:9627–38. DOI: 10.1109/TPAMI.2023.3240565LiuDWangJShanQSmylDDengJ and DuJ.DeepEIT: Deep Image Prior Enabled Electrical Impedance Tomography.2023Aug;45:9627–38. DOI:10.1109/TPAMI.2023.3240565Open DOISearch in Google Scholar
Murbach MD and Schwartz DT. Open Software and Datasets for the Analysis of Electrochemical Impedance Spectra. Electrochem. Soc. Interface 2019 Jan; 28:51. DOI: 10.1149/2.F05191ifMurbachMD and SchwartzDT.Open Software and Datasets for the Analysis of Electrochemical Impedance Spectra.2019Jan;28:51. DOI:10.1149/2.F05191ifOpen DOISearch in Google Scholar
Chen X, Roberts R, Liu Z and Tong W. A Generative Adversarial Network Model Alternative to Animal Studies for Clinical Pathology Assessment. Nat Commun 2023 Nov; 14:7141. DOI: 10.1038/s41467-023-42933-9ChenXRobertsRLiuZ and TongW.A Generative Adversarial Network Model Alternative to Animal Studies for Clinical Pathology Assessment.2023Nov;14:7141. DOI:10.1038/s41467-023-42933-9Open DOISearch in Google Scholar
Guttulsrud H. Generating Synthetic Medical Images with 3D GANs. MA thesis. Oslomet - storbyuniversitetet, 2023GuttulsrudH..MA thesis. Oslomet - storbyuniversitetet,2023Search in Google Scholar
Mensing D, Hirsch J, Wenzel M and Günther M. 3D (c)GAN for Whole Body MR Synthesis. Deep Generative Models: Second MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Berlin, Heidelberg: Springer-Verlag, 2022 Sep :97–105. DOI: 10.1007/978-3-031-18576-2_10MensingDHirschJWenzelM and GüntherM.3D (c)GAN for Whole Body MR Synthesis..Berlin, Heidelberg:Springer-Verlag,2022Sep:97–105. DOI:10.1007/978-3-031-18576-2_10Open DOISearch in Google Scholar
Schaefferkoetter J, Yan J, Moon S, Chan R, Ortega C, Metser U, Berlin A and Veit-Haibach P. Deep Learning for Whole-Body Medical Image Generation. Eur J Nucl Med Mol Imaging 2021 Nov; 48:3817–26. DOI: 10.1007/s00259-021-05413-0SchaefferkoetterJYanJMoonSChanROrtegaCMetserUBerlinA and Veit-HaibachP.Deep Learning for Whole-Body Medical Image Generation.2021Nov;48:3817–26. DOI:10.1007/s00259-021-05413-0Open DOISearch in Google Scholar
Wu C, Zhang H, Chen J, Gao Z, Zhang P, Muhammad K and Del Ser J. Vessel-GAN: Angiographic Reconstructions from Myocardial CT Perfusion with Explainable Generative Adversarial Networks. Future Generation Computer Systems 2022 May; 130:128–39. DOI: 10.1016/j.future.2021.12.007WuCZhangHChenJGaoZZhangPMuhammadK and Del SerJ.Vessel-GAN: Angiographic Reconstructions from Myocardial CT Perfusion with Explainable Generative Adversarial Networks.2022May;130:128–39. DOI:10.1016/j.future.2021.12.007Open DOISearch in Google Scholar
Moeyersons J, Morales J, Seeuws N, Van Hoof C, Hermeling E, Groenendaal W, Willems R, Van Huffel S and Varon C. Artefact Detection in Impedance Pneumography Signals: A Machine Learning Approach. Sensors 2021 Jan; 21:2613. DOI: 10.3390/s21082613MoeyersonsJMoralesJSeeuwsNVan HoofCHermelingEGroenendaalWWillemsRVan HuffelS and VaronC.Artefact Detection in Impedance Pneumography Signals: A Machine Learning Approach.2021Jan;21:2613. DOI:10.3390/s21082613Open DOISearch in Google Scholar
Smyl D and Liu D. Optimizing Electrode Positions in 2-D Electrical Impedance Tomography Using Deep Learning. IEEE Transactions on Instrumentation and Measurement 2020 Sep; 69:6030–44. DOI: 10.1109/TIM.2020.2970371SmylD and LiuD.Optimizing Electrode Positions in 2-D Electrical Impedance Tomography Using Deep Learning.2020Sep;69:6030–44. DOI:10.1109/TIM.2020.2970371Open DOISearch in Google Scholar
Wang H, Xu G and Zhou Q. A Comparative Study of Variational Autoencoders, Normalizing Flows, and Score-Based Diffusion Models for Electrical Impedance Tomography. Journal of Inverse and Ill-posed Problems 2024 Jan. DOI: 10.1515/jiip-2023-0037WangHXuG and ZhouQ.A Comparative Study of Variational Autoencoders, Normalizing Flows, and Score-Based Diffusion Models for Electrical Impedance Tomography.2024Jan. DOI:10.1515/jiip-2023-0037Open DOISearch in Google Scholar