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Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine

IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 859-876页

作者： Ahmad Chaddad Qizong Lu Jiali Li yousef katib Reem Kateb Camel Tanougast Ahmed Bouridane Ahmed Abdulkadir School of Artificial intelligence Guilin Universiy of Electronic TechnologyGuilin 541004China Laboratory for Imagery Vision and Artificial IntelligenceEcole de Technologie SuperieureMontrealQuebec H3C 1K3Canada College of Medicine Taibah UniversityMadinah 42353Saudi Arabia College of Computer Science and Engineering Taibah UniversityMadinah 42353Saudi Arabia Laboratory of Design Optimization and Modeling SystemsUniversity of LorraineMetz 57070France Center for Data Analytics and Cybersecurity(CDAC) University of SharjahSharjah 27272United Arab Emirates Laboratoire de recherche en neuroimagerie Centre Hospitalier Universitaire VaudoisMont Paisible 161011LausanneSwitzerland

Artificial intelligence(AI)continues to transform data analysis in many *** in each domain is driven by a growing body of annotated data,increased computational resources,and technological *** medicine,the sensitivity of the data,the complexity of the tasks,the potentially high stakes,and a requirement of accountability give rise to a particular set of *** this review,we focus on three key methodological approaches that address some of the particular challenges in AI-driven medical decision making.1)Explainable AI aims to produce a human-interpretable justification for each *** models increase confidence if the results appear plausible and match the clinicians ***,the absence of a plausible explanation does not imply an inaccurate *** in highly non-linear,complex models that are tuned to maximize accuracy,such interpretable representations only reflect a small portion of the justification.2)Domain adaptation and transfer learning enable AI models to be trained and applied across multiple *** example,a classification task based on images acquired on different acquisition hardware.3)Federated learning enables learning large-scale models without exposing sensitive personal health *** centralized AI learning,where the centralized learning machine has access to the entire training data,the federated learning process iteratively updates models across multiple sites by exchanging only parameter updates,not personal health *** narrative review covers the basic concepts,highlights relevant corner-stone and stateof-the-art research in the field,and discusses perspectives.

关键词： Domain adaptation explainable artificial intelligence federated learning

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Simple approach for the histomolecular diagnosis of central nervous system gliomas based on 2021 World Health Organization Classification

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World Journal of Clinical Oncology 2022年第7期13卷 567-576页

作者： Maher Kurdi Rana H Moshref yousef katib Eyad Faizo Ahmed A Najjar Basem Bahakeem Ahmed KBamaga Department of Pathology Faculty of MedicineKing Abdulaziz UniversityJeddah 213733Saudi Arabia Department of Neurosciences King Faisal Specialist Hospital and Research CenterJeddah 213733Saudi Arabia Department of Radiology Faculty of MedicineTaibah UniversityAlmadinah Almunawwarah 213733Saudi Arabia Division of Neurosurgery Department of SurgeryFaculty of MedicineTabuk UniversityTabuk 213733Saudi Arabia College of Medicine Taibah UniversityAlmadinah Almunawwarah 213733Saudi Arabia Faculty of Medicine Umm-Alqura UniversityMakkah 213733Saudi Arabia Department of Pediatric Neuromuscular Medicine UnitFaculty of Medicine and King Abdulaziz University HospitalKing Abdulaziz UniversityJeddah 213733Saudi Arabia

The classification of central nervous system(CNS)glioma went through a sequence of developments,between 2006 and 2021,started with only histological approach then has been aided with a major emphasis on molecular signatures in the 4^(th) and 5^(th) editions of the World Health Organization(WHO).The recent reformation in the 5th edition of the WHO classification has focused more on the molecularly defined entities with better characterized natural histories as well as new tumor types and subtypes in the adult and pediatric *** new subclassified entities have been incorporated in the 5^(th) edition after the continuous exploration of new genomic,epigenomic and transcriptomic ***,the current guidelines of 2021 WHO classification of CNS tumors and European Association of Neuro-Oncology(EANO)exploited the molecular signatures in the diagnostic approach of CNS *** current review presents a practical diagnostic approach for diffuse CNS gliomas and circumscribed astrocytomas using histomolecular criteria adopted by the recent WHO *** also describe the treatment strategies for these tumors based on EANO guidelines.

关键词： Central Nervous System glioma Classification World Health Organization 2021 European Association of Neuro-Oncology guidelines

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Endovascular Repair of Abdominal Aortic Aneurysm May Not Provide Long-Term Survival Benefit

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Surgical Science 2016年第7期7卷 316-320页

作者： R. Englund N. katib Department of Vascular Surgery St George Private Hospital Kogarah Australia

Purpose: To investigate the long term survival benefit for the treatment of abdominal aortic aneurysmal (AAA) disease with Endovascular Aneurysm Repair (EVAR). Our hypothesis is that patients undergoing EVAR have an equivalent survival to an age matched population. Methods: Between 1997 and 2014 all patients treated for an AAA with EVAR by a single surgeon were prospectively followed and at 12 monthly intervals ultrasonography was utilised to assess the size of the aorta after treatment. Data from the Births, Deaths and Marriages was also collected regarding the survival of these patients. As of 2003 all patients with AAA were treated electively with EVAR. Results: There were a total of 145 patients treated with EVAR for AAA during the study period, 143 who survived the 30-day post-operative period. The long-term survival following EVAR at one year was 95% (95% C.I.: 88% to 97%), at 5 years 56% (95% C.I.: 42% to 64%), and at 10 years 28% (95% C.I.: 17% to 42%). Compared to an age-matched population the EVAR group had a lower than expected long term survival (P < 0.0001). Discussion: EVAR for the treatment of abdominal aortic aneurysms can be achieved with acceptable 30 day survival and freedom from aneurysm related death. However selection of patients for EVAR may significantly effect long-term survival if patients are fit for EVAR but not open operation. The question arises as to whether EVAR should be performed in patients with questionable long-term survival, even if they are fit for this procedure.

关键词： Endovascular Abdominal Aortic Aneurysm Survival

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Heap Based Optimization with Deep Quantum Neural Network Based Decision Making on Smart Healthcare Applications

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Computer Systems Science & Engineering 2023年第9期46卷 3749-3765页

作者： Iyad katib Mahmoud Ragab Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

The concept of smart healthcare has seen a gradual increase with the expansion of information *** healthcare will use a new generation of information technologies,like artificial intelligence,the Internet of Things(IoT),cloud computing,and big data,to transformthe conventional medical system in an all-around way,making healthcare highly effective,more personalized,and more *** work designs a new Heap Based Optimization with Deep Quantum Neural Network(HBO-DQNN)model for decision-making in smart healthcare *** presented HBO-DQNN modelmajorly focuses on identifying and classifying healthcare *** the presented HBO-DQNN model,three stages of operations were *** normalization is applied to pre-process the input data at the initial ***,the HBO algorithm is used in the second stage to choose an optimal set of features from the healthcare *** last,the DQNN model is exploited for healthcare data classification.A series of experiments were carried out to portray the promising classifier results of the HBO-DQNN *** extensive comparative study reported the improvements of the HBO-DQNN method over other existing models with maximum accuracy of 97.05%and 95.72%under the colon cancer and lymphoma dataset.

关键词： Heap-based optimization smart healthcare decision making intelligent models artificial intelligence

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