Pub Date : 2023-09-01DOI: 10.1016/j.jmir.2023.06.122
Kumar Shamie
{"title":"ADVANCEMENTS IN AI TRAINING AND EDUCATION FOR A FUTURE-READY HEALTHCARE SYSTEM","authors":"Kumar Shamie","doi":"10.1016/j.jmir.2023.06.122","DOIUrl":"https://doi.org/10.1016/j.jmir.2023.06.122","url":null,"abstract":"","PeriodicalId":94092,"journal":{"name":"Journal of medical imaging and radiation sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47064319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1016/j.jmir.2023.06.133
F. A. Hadi, F. W. A. Zaiki
{"title":"SAFETY AND ACCURACY OF MULTIPLE PRENATAL ULTRASOUND EXAMINATIONS FOR FETAL GENDER IDENTIFICATION: A SCOPING REVIEW","authors":"F. A. Hadi, F. W. A. Zaiki","doi":"10.1016/j.jmir.2023.06.133","DOIUrl":"https://doi.org/10.1016/j.jmir.2023.06.133","url":null,"abstract":"","PeriodicalId":94092,"journal":{"name":"Journal of medical imaging and radiation sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44373998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1016/j.jmir.2023.06.113
P. Wulandari, Ppw Gitawiarsa, Putu Adi Susanta
{"title":"A COMPREHENSIVE REVIEW OF INDONESIAN DIAGNOSTIC REFERENCE LEVELS (IDRLs) FOR CT SCAN EXAMINATIONS","authors":"P. Wulandari, Ppw Gitawiarsa, Putu Adi Susanta","doi":"10.1016/j.jmir.2023.06.113","DOIUrl":"https://doi.org/10.1016/j.jmir.2023.06.113","url":null,"abstract":"","PeriodicalId":94092,"journal":{"name":"Journal of medical imaging and radiation sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49012753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1016/j.jmir.2023.06.108
Mohamad Asmawi Mohamad Ariffin, M. Karim
{"title":"DIAGNOSTIC REFERENCE LEVEL OF RADIATION DOSE AND IMAGE QUALITY OF ADULT CT ABDOMINAL EXAMINATIONS IN A TERTIARY HOSPITAL IN MALAYSIA","authors":"Mohamad Asmawi Mohamad Ariffin, M. Karim","doi":"10.1016/j.jmir.2023.06.108","DOIUrl":"https://doi.org/10.1016/j.jmir.2023.06.108","url":null,"abstract":"","PeriodicalId":94092,"journal":{"name":"Journal of medical imaging and radiation sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47236520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1016/j.jmir.2023.06.117
Roslan Nurul Ashikin, Setiawan Agung Nugroho, Norzan Muhammad Ammar, Jasmin Nur Hayati
OBJECTIVE We aimed to assess the application of Artificial Intelligence (AI) methodologies for scar segmentation in cardiac magnetic resonance (CMR) imaging and its performance evaluation. MATERIALS & METHODS Following PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) guidelines, a systematic search of PubMed and Science Direct was undertaken from 2012 to 2022 to search for full-text publications that implemented AI methods on scar segmentation in CMR in patients with cardiovascular diseases. RESULTS A total of 21 articles out of 475 articles were selected for the final review. Supervised deep learning and unsupervised machine learning were implemented in 16 (76.2%) and 5 (23.8%) articles respectively, favouring learning methods. Dice similarity coefficient (DSC) value was used as measures of the performance of AI methods in 19 articles. Supervised and unsupervised learning models had similar DSC compared to manual segmentation with a score of 0.74, 95% confidence interval (CI) [0.67, 0.81] vs 0.71, 95% CI [0.63, 0.79], P = 0.35). The application of AI has been advanced with the emerging of sophisticated algorithms allowing for quantification of border zone and microvascular obstruction regions. The performance of AI method is highly depending on the network architecture, training strategies, and data set used for training. CONCLUSION The presence of AI methods in scar segmentation demonstrated high feasibility with good performance evaluation for quantifying myocardial scar. This study can have a huge impact on clinicians in health care by improving their experiences with scar segmentation and enhancing clinically validated application of AI in CMR imaging. We aimed to assess the application of Artificial Intelligence (AI) methodologies for scar segmentation in cardiac magnetic resonance (CMR) imaging and its performance evaluation. Following PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) guidelines, a systematic search of PubMed and Science Direct was undertaken from 2012 to 2022 to search for full-text publications that implemented AI methods on scar segmentation in CMR in patients with cardiovascular diseases. A total of 21 articles out of 475 articles were selected for the final review. Supervised deep learning and unsupervised machine learning were implemented in 16 (76.2%) and 5 (23.8%) articles respectively, favouring learning methods. Dice similarity coefficient (DSC) value was used as measures of the performance of AI methods in 19 articles. Supervised and unsupervised learning models had similar DSC compared to manual segmentation with a score of 0.74, 95% confidence interval (CI) [0.67, 0.81] vs 0.71, 95% CI [0.63, 0.79], P = 0.35). The application of AI has been advanced with the emerging of sophisticated algorithms allowing for quantification of border zone and microvascular obstruction regions. The performance of AI method is highly depending on the network architecture, training
目的探讨人工智能(AI)方法在心脏磁共振(CMR)成像中疤痕分割的应用及其性能评价。材料和方法遵循PRISMA(系统评价和荟萃分析的首选报告项目)指南，在2012年至2022年期间对PubMed和Science Direct进行了系统搜索，以搜索在心血管疾病患者的CMR中应用AI方法进行疤痕分割的全文出版物。结果475篇文献中，共有21篇入选终审稿。有监督深度学习和无监督机器学习分别在16篇(76.2%)和5篇(23.8%)文章中实现，有利于学习方法。在19篇文章中，采用骰子相似系数(DSC)值作为人工智能方法性能的度量。与人工分割相比，有监督学习和无监督学习模型的DSC相似，得分为0.74,95%置信区间(CI) [0.67, 0.81] vs 0.71, 95% CI [0.63, 0.79]， P = 0.35)。随着复杂算法的出现，人工智能的应用已经取得了进展，可以对边界区和微血管阻塞区域进行量化。人工智能方法的性能高度依赖于网络架构、训练策略和用于训练的数据集。结论人工智能方法在瘢痕分割中应用于心肌瘢痕定量具有较高的可行性和良好的性能评价。这项研究可以通过改善他们在疤痕分割方面的经验和加强人工智能在CMR成像中的临床验证应用，对医疗保健临床医生产生巨大影响。我们旨在评估人工智能(AI)方法在心脏磁共振(CMR)成像中疤痕分割的应用及其性能评估。遵循PRISMA(系统评价和荟萃分析的首选报告项目)指南，从2012年到2022年，对PubMed和Science Direct进行了系统搜索，以搜索在心血管疾病患者的CMR中应用AI方法进行疤痕分割的全文出版物。从475篇文章中选出21篇文章进行最终审查。有监督深度学习和无监督机器学习分别在16篇(76.2%)和5篇(23.8%)文章中实现，有利于学习方法。在19篇文章中，采用骰子相似系数(DSC)值作为人工智能方法性能的度量。与人工分割相比，有监督学习和无监督学习模型的DSC相似，得分为0.74,95%置信区间(CI) [0.67, 0.81] vs 0.71, 95% CI [0.63, 0.79]， P = 0.35)。随着复杂算法的出现，人工智能的应用已经取得了进展，可以对边界区和微血管阻塞区域进行量化。人工智能方法的性能高度依赖于网络架构、训练策略和用于训练的数据集。人工智能方法在疤痕分割中的应用，对心肌疤痕量化具有较高的可行性和良好的性能评价。这项研究可以通过改善他们在疤痕分割方面的经验和加强人工智能在CMR成像中的临床验证应用，对医疗保健临床医生产生巨大影响。
{"title":"THE APPLICATION OF ARTIFICIAL INTELLIGENCE ON SCAR SEGMENTATION IN CARDIAC MAGNETIC RESONANCE IMAGING: A SYSTEMATIC LITERATURE REVIEW","authors":"Roslan Nurul Ashikin, Setiawan Agung Nugroho, Norzan Muhammad Ammar, Jasmin Nur Hayati","doi":"10.1016/j.jmir.2023.06.117","DOIUrl":"https://doi.org/10.1016/j.jmir.2023.06.117","url":null,"abstract":"OBJECTIVE We aimed to assess the application of Artificial Intelligence (AI) methodologies for scar segmentation in cardiac magnetic resonance (CMR) imaging and its performance evaluation. MATERIALS & METHODS Following PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) guidelines, a systematic search of PubMed and Science Direct was undertaken from 2012 to 2022 to search for full-text publications that implemented AI methods on scar segmentation in CMR in patients with cardiovascular diseases. RESULTS A total of 21 articles out of 475 articles were selected for the final review. Supervised deep learning and unsupervised machine learning were implemented in 16 (76.2%) and 5 (23.8%) articles respectively, favouring learning methods. Dice similarity coefficient (DSC) value was used as measures of the performance of AI methods in 19 articles. Supervised and unsupervised learning models had similar DSC compared to manual segmentation with a score of 0.74, 95% confidence interval (CI) [0.67, 0.81] vs 0.71, 95% CI [0.63, 0.79], P = 0.35). The application of AI has been advanced with the emerging of sophisticated algorithms allowing for quantification of border zone and microvascular obstruction regions. The performance of AI method is highly depending on the network architecture, training strategies, and data set used for training. CONCLUSION The presence of AI methods in scar segmentation demonstrated high feasibility with good performance evaluation for quantifying myocardial scar. This study can have a huge impact on clinicians in health care by improving their experiences with scar segmentation and enhancing clinically validated application of AI in CMR imaging. We aimed to assess the application of Artificial Intelligence (AI) methodologies for scar segmentation in cardiac magnetic resonance (CMR) imaging and its performance evaluation. Following PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) guidelines, a systematic search of PubMed and Science Direct was undertaken from 2012 to 2022 to search for full-text publications that implemented AI methods on scar segmentation in CMR in patients with cardiovascular diseases. A total of 21 articles out of 475 articles were selected for the final review. Supervised deep learning and unsupervised machine learning were implemented in 16 (76.2%) and 5 (23.8%) articles respectively, favouring learning methods. Dice similarity coefficient (DSC) value was used as measures of the performance of AI methods in 19 articles. Supervised and unsupervised learning models had similar DSC compared to manual segmentation with a score of 0.74, 95% confidence interval (CI) [0.67, 0.81] vs 0.71, 95% CI [0.63, 0.79], P = 0.35). The application of AI has been advanced with the emerging of sophisticated algorithms allowing for quantification of border zone and microvascular obstruction regions. The performance of AI method is highly depending on the network architecture, training","PeriodicalId":94092,"journal":{"name":"Journal of medical imaging and radiation sciences","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136265481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1016/j.jmir.2023.06.137
Chen Semaya Natalia, Ong Xue Jing, Sin Sze Yarn
{"title":"EFFECT OF INDUCTION CHEMOTHERAPY ON GROSS TUMOUR VOLUME REDUCTION AND ORGAN DOSES IN ADVANCED-STAGE NASOPHARYNGEAL CARCINOMA: CORRELATION WITH EPSTEIN-BARR VIRUS DNA LEVELS","authors":"Chen Semaya Natalia, Ong Xue Jing, Sin Sze Yarn","doi":"10.1016/j.jmir.2023.06.137","DOIUrl":"https://doi.org/10.1016/j.jmir.2023.06.137","url":null,"abstract":"","PeriodicalId":94092,"journal":{"name":"Journal of medical imaging and radiation sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47823296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"RADIATION EXPOSURE RATE REDUCTION IN MOBILE X-RAY EXAMINATION EQUIPPED WITH A BLUETOOTH WIRELESS EXPOSURE SWITCH:A PRE-EXPERIMENTAL STUDY","authors":"Sulistiyadi Akhmad Haris, Wibowo Ardi Soesilo, Rochmayanti Dwi, Kartikasari Yeti, S. Sugiyanto, Ardiyanto Jeffri, Rasyid Rasyid, Nurbaiti Salis","doi":"10.1016/j.jmir.2023.06.136","DOIUrl":"https://doi.org/10.1016/j.jmir.2023.06.136","url":null,"abstract":"","PeriodicalId":94092,"journal":{"name":"Journal of medical imaging and radiation sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47852108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1016/j.jmir.2023.06.141
M. Mazlyfarina, Sallehuddin Afrina, Mohamad Ariffin Elyqa Farra, Mohamad Shahimin Mizhanim, A. R. Rogayah, A. A. Azmarul, Leong Yuh Yang, Sahnan Norshuhada
{"title":"MAPPING SENTENCE COMPREHENSION TASK IN HEALTHY COGNITIVE AGEING (HCA): A fMRI STUDY","authors":"M. Mazlyfarina, Sallehuddin Afrina, Mohamad Ariffin Elyqa Farra, Mohamad Shahimin Mizhanim, A. R. Rogayah, A. A. Azmarul, Leong Yuh Yang, Sahnan Norshuhada","doi":"10.1016/j.jmir.2023.06.141","DOIUrl":"https://doi.org/10.1016/j.jmir.2023.06.141","url":null,"abstract":"","PeriodicalId":94092,"journal":{"name":"Journal of medical imaging and radiation sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44792348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1016/j.jmir.2023.06.118
Ahmad Sabri Afiq, M. Shafini, Nor Azman Nurul Dizyana, Mahmud Mohd Hafizi
{"title":"CONTRAST ENHANCEMENT AND IMAGE QUALITY ACCEPTABILITY OF COMPUTED TOMOGRAPHY PULMONARY ANGIOGRAPHY USING AUTOMATIC BOLUS TRACKING AND TEST BOLUS CONTRAST INJECTION TECHNIQUES","authors":"Ahmad Sabri Afiq, M. Shafini, Nor Azman Nurul Dizyana, Mahmud Mohd Hafizi","doi":"10.1016/j.jmir.2023.06.118","DOIUrl":"https://doi.org/10.1016/j.jmir.2023.06.118","url":null,"abstract":"","PeriodicalId":94092,"journal":{"name":"Journal of medical imaging and radiation sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48713004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"THE CONTRAST-ENHANCE SEQUENCE AND 3D TIME-OF-FLIGHT SEQUENCE COMPARISON ON ANEURISMS MR ANGIOGRAPHY EXAMINATION : STUDI LITERATURE REVIEW","authors":"Lutfiani Sintia Aprilina, Susanto Edy, Murniati Emi, Halim Kelvin","doi":"10.1016/j.jmir.2023.06.128","DOIUrl":"https://doi.org/10.1016/j.jmir.2023.06.128","url":null,"abstract":"","PeriodicalId":94092,"journal":{"name":"Journal of medical imaging and radiation sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48806310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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