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  1. Article: Management of Fourniers gangrene secondary to perineal invasion by rectal cancer.

    Al-Bahri, Shadi S / Mousa, Hussam M

    International journal of surgery case reports

    2023  Volume 104, Page(s) 107955

    Abstract: Introduction and importance: Fournier's gangrene is a known disease process resulting in a severe necrotizing soft tissue infection involving the perineum and scrotum. Although most cases are known to be associated with diabetes (Go et al., 2010 [1]), ... ...

    Abstract Introduction and importance: Fournier's gangrene is a known disease process resulting in a severe necrotizing soft tissue infection involving the perineum and scrotum. Although most cases are known to be associated with diabetes (Go et al., 2010 [1]), it is rare to develop this extensive infection secondary to tumor invasion from the rectum. Treatment typically requires several debridements until infection is fully controlled.
    Case presentation: A 65 year old man with a history of locally invasive and unresectable rectal cancer presents to our emergency department with severe perineal and scrotal pain and was found to be in septic shock. He had previously undergone a diverting colostomy as well as radiation to the pelvis. He underwent several surgical debridements until the infection was controlled. He then required procedures to close the large defects created until complete wound healing was achieved within 3 months of presentation.
    Clinical discussion: This condition is associated with a high morbidity and mortality, and its management can be split in to two stages. The early phase includes resuscitation, initial debridements and likely several sequential debridements as well as fecal diversion. The late phase then involves the healing process with reconstruction efforts. A multi-disciplinary team is required for appropriate management under the direction of the general surgeon, which also include urologists, plastic surgeons and wound care nurses.
    Conclusion: Fournier's gangrene secondary to tumor invasion should be recognized as a potential cause other than the typical culprits. Resuscitation, antibiotics, debridements and a team approach is needed to recover from such a debilitating disease.
    Language English
    Publishing date 2023-03-02
    Publishing country Netherlands
    Document type Case Reports
    ISSN 2210-2612
    ISSN 2210-2612
    DOI 10.1016/j.ijscr.2023.107955
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology.

    Albahri, A S / Al-Qaysi, Z T / Alzubaidi, Laith / Alnoor, Alhamzah / Albahri, O S / Alamoodi, A H / Bakar, Anizah Abu

    International journal of telemedicine and applications

    2023  Volume 2023, Page(s) 7741735

    Abstract: The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and ... ...

    Abstract The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (
    Language English
    Publishing date 2023-04-30
    Publishing country Egypt
    Document type Journal Article ; Review
    ZDB-ID 2397787-5
    ISSN 1687-6423 ; 1687-6415
    ISSN (online) 1687-6423
    ISSN 1687-6415
    DOI 10.1155/2023/7741735
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Diagnosis-Based Hybridization of Multimedical Tests and Sociodemographic Characteristics of Autism Spectrum Disorder Using Artificial Intelligence and Machine Learning Techniques: A Systematic Review.

    Alqaysi, M E / Albahri, A S / Hamid, Rula A

    International journal of telemedicine and applications

    2022  Volume 2022, Page(s) 3551528

    Abstract: Autism spectrum disorder (ASD) is a complex neurobehavioral condition that begins in childhood and continues throughout life, affecting communication and verbal and behavioral skills. It is challenging to discover autism in the early stages of life, ... ...

    Abstract Autism spectrum disorder (ASD) is a complex neurobehavioral condition that begins in childhood and continues throughout life, affecting communication and verbal and behavioral skills. It is challenging to discover autism in the early stages of life, which prompted researchers to intensify efforts to reach the best solutions to treat this challenge by introducing artificial intelligence (AI) techniques and machine learning (ML) algorithms, which played an essential role in greatly assisting the medical and healthcare staff and trying to obtain the highest predictive results for autism spectrum disorder. This study is aimed at systematically reviewing the literature related to the criteria, including multimedical tests and sociodemographic characteristics in AI techniques and ML contributions. Accordingly, this study checked the Web of Science (WoS), Science Direct (SD), IEEE Xplore digital library, and Scopus databases. A set of 944 articles from 2017 to 2021 is collected to reveal a clear picture and better understand all the academic literature through a definitive collection of 40 articles based on our inclusion and exclusion criteria. The selected articles were divided based on similarity, objective, and aim evidence across studies. They are divided into two main categories: the first category is "diagnosis of ASD based on questionnaires and sociodemographic features" (
    Language English
    Publishing date 2022-07-01
    Publishing country Egypt
    Document type Journal Article ; Review
    ZDB-ID 2397787-5
    ISSN 1687-6423 ; 1687-6415
    ISSN (online) 1687-6423
    ISSN 1687-6415
    DOI 10.1155/2022/3551528
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Hybrid Diagnosis Models for Autism Patients Based on Medical and Sociodemographic Features Using Machine Learning and Multicriteria Decision-Making (MCDM) Techniques: An Evaluation and Benchmarking Framework.

    Alqaysi, M E / Albahri, A S / Hamid, Rula A

    Computational and mathematical methods in medicine

    2022  Volume 2022, Page(s) 9410222

    Abstract: Method: The three-phase framework integrated the MCDM and ML to develop the diagnosis models and evaluate and benchmark the best. Firstly, the new ASD-dataset-combined medical tests and sociodemographic characteristic features is identified and ... ...

    Abstract Method: The three-phase framework integrated the MCDM and ML to develop the diagnosis models and evaluate and benchmark the best. Firstly, the new ASD-dataset-combined medical tests and sociodemographic characteristic features is identified and preprocessed. Secondly, developing the hybrid diagnosis models using the intersection process between three FS techniques and five ML algorithms introduces 15 models. The selected medical tests and sociodemographic features from each FS technique are weighted before feeding the five ML algorithms using the fuzzy-weighted zero-inconsistency (FWZIC) method based on four psychiatry experts. Thirdly, (i) formulate a dynamic decision matrix for all developed models based on seven evaluation metrics, including classification accuracy, precision, F1 score, recall, test time, train time, and AUC. (ii) The fuzzy decision by opinion score method (FDOSM) is used to evaluate and benchmark the 15 models concerning the seven evaluation metrics.
    Results: Results reveal that (i) the three FS techniques have obtained a size different from the others in the number of the selected features; the sets were 39, 38, and 41 out of 48 features. Each set has its weights constructed by FWIZC. Considered sociodemographic features have been mostly selected more than medical tests within FS techniques. (ii) The first three best hybrid models were "ReF-decision tree," "IG-decision tree," and "Chi
    MeSH term(s) Humans ; Benchmarking ; Autistic Disorder/diagnosis ; Machine Learning ; Algorithms
    Language English
    Publishing date 2022-11-16
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2252430-7
    ISSN 1748-6718 ; 1748-670X ; 1027-3662
    ISSN (online) 1748-6718
    ISSN 1748-670X ; 1027-3662
    DOI 10.1155/2022/9410222
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Triage and priority-based healthcare diagnosis using artificial intelligence for autism spectrum disorder and gene contribution: A systematic review.

    Joudar, Shahad Sabbar / Albahri, A S / Hamid, Rula A

    Computers in biology and medicine

    2022  Volume 146, Page(s) 105553

    Abstract: The exact nature, harmful effects and aetiology of autism spectrum disorder (ASD) have caused widespread confusion. Artificial intelligence (AI) science helps solve challenging diagnostic problems in the medical field through extensive experiments. ... ...

    Abstract The exact nature, harmful effects and aetiology of autism spectrum disorder (ASD) have caused widespread confusion. Artificial intelligence (AI) science helps solve challenging diagnostic problems in the medical field through extensive experiments. Disease severity is closely related to triage decisions and prioritisation contexts in medicine because both have been widely used to diagnose various diseases via AI, machine learning and automated decision-making techniques. Recently, taking advantage of high-performance AI algorithms has achieved accessible success in diagnosing and predicting risks from clinical and biological data. In contrast, less progress has been made with ASD because of obscure reasons. According to academic literature, ASD diagnosis works from a specific perspective, and much of the confusion arises from the fact that how AI techniques are currently integrated with the diagnosis of ASD concerning the triage and priority strategies and gene contributions. To this end, this study sought to describe a systematic review of the literature to assess the respective AI methods using the available datasets, highlight the tools and strategies used for diagnosing ASD and investigate how AI trends contribute in distinguishing triage and priority for ASD and gene contributions. Accordingly, this study checked the Science Direct, IEEE Xplore Digital Library, Web of Science (WoS), PubMed, and Scopus databases. A set of 363 articles from 2017 to 2022 is collected to reveal a clear picture and a better understanding of all the academic literature through a final set of 18 articles. The retrieved articles were filtered according to the defined inclusion and exclusion criteria and classified into three categories. The first category includes 'Triage patients based on diagnosis methods' which accounts for 16.66% (n = 3/18). The second category includes 'Prioritisation for Risky Genes' which accounts for 66.6% (n = 12/18) and is classified into two subcategories: 'Mutations observation based', 'Biomarkers and toxic chemical observations'. The third category includes 'E-triage using telehealth' which accounts for 16.66% (n = 3/18). This multidisciplinary systematic review revealed the taxonomy, motivations, recommendations and challenges of ASD research that need synergistic attention. Thus, this systematic review performs a comprehensive science mapping analysis and discusses the open issues that help perform and improve the recommended solution of ASD research direction. In addition, this study critically reviews the literature and attempts to address the current research gaps in knowledge and highlights weaknesses that require further research. Finally, a new developed methodology has been suggested as future work for triaging and prioritising ASD patients according to their severity levels by using decision-making techniques.
    MeSH term(s) Artificial Intelligence ; Autism Spectrum Disorder/diagnosis ; Autism Spectrum Disorder/genetics ; Humans ; Machine Learning ; Telemedicine/methods ; Triage/methods
    Language English
    Publishing date 2022-05-09
    Publishing country United States
    Document type Journal Article ; Systematic Review
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2022.105553
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy-TOPSIS methods.

    Albahri, A S / Hamid, Rula A / Albahri, O S / Zaidan, A A

    Artificial intelligence in medicine

    2020  Volume 111, Page(s) 101983

    Abstract: Context and background: Corona virus (COVID) has rapidly gained a foothold and caused a global pandemic. Particularists try their best to tackle this global crisis. New challenges outlined from various medical perspectives may require a novel design ... ...

    Abstract Context and background: Corona virus (COVID) has rapidly gained a foothold and caused a global pandemic. Particularists try their best to tackle this global crisis. New challenges outlined from various medical perspectives may require a novel design solution. Asymptomatic COVID-19 carriers show different health conditions and no symptoms; hence, a differentiation process is required to avert the risk of chronic virus carriers.
    Objectives: Laboratory criteria and patient dataset are compulsory in constructing a new framework. Prioritisation is a popular topic and a complex issue for patients with COVID-19, especially for asymptomatic carriers due to multi-laboratory criteria, criterion importance and trade-off amongst these criteria. This study presents new integrated decision-making framework that handles the prioritisation of patients with COVID-19 and can detect the health conditions of asymptomatic carriers.
    Methods: The methodology includes four phases. Firstly, eight important laboratory criteria are chosen using two feature selection approaches. Real and simulation datasets from various medical perspectives are integrated to produce a new dataset involving 56 patients with different health conditions and can be used to check asymptomatic cases that can be detected within the prioritisation configuration. The first phase aims to develop a new decision matrix depending on the intersection between 'multi-laboratory criteria' and 'COVID-19 patient list'. In the second phase, entropy is utilised to set the objective weight, and TOPSIS is adapted to prioritise patients in the third phase. Finally, objective validation is performed.
    Results: The patients are prioritised based on the selected criteria in descending order of health situation starting from the worst to the best. The proposed framework can discriminate among mild, serious and critical conditions and put patients in a queue while considering asymptomatic carriers. Validation findings revealed that the patients are classified into four equal groups and showed significant differences in their scores, indicating the validity of ranking.
    Conclusions: This study implies and discusses the numerous benefits of the suggested framework in detecting/recognising the health condition of patients prior to discharge, supporting the hospitalisation characteristics, managing patient care and optimising clinical prediction rule.
    MeSH term(s) Adult ; Aged ; COVID-19/physiopathology ; Carrier State/physiopathology ; Computer Simulation ; Decision Support Techniques ; Diagnostic Techniques and Procedures/statistics & numerical data ; Female ; Humans ; Male ; Middle Aged ; Reproducibility of Results ; Risk Factors ; SARS-CoV-2 ; Time Factors
    Language English
    Publishing date 2020-11-07
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 645179-2
    ISSN 1873-2860 ; 0933-3657
    ISSN (online) 1873-2860
    ISSN 0933-3657
    DOI 10.1016/j.artmed.2020.101983
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Telemedicine perception and interest among medical students at the University of Sharjah, United Arab Emirates, 2023.

    Albahri, Abdulaziz H / Alnaqbi, Shatha A / Alnaqbi, Shahad A / Shorbagi, Sarra

    BMC medical education

    2023  Volume 23, Issue 1, Page(s) 892

    Abstract: Background: Telemedicine is becoming an integral part of healthcare. Training medical students in telemedicine is encouraged by many medical organizations. However, in the United Arab Emirates in particular, most medical schools have not incorporated it ...

    Abstract Background: Telemedicine is becoming an integral part of healthcare. Training medical students in telemedicine is encouraged by many medical organizations. However, in the United Arab Emirates in particular, most medical schools have not incorporated it into their curriculum. Therefore, this study aims to assess medical students' perceptions and interest in telemedicine teaching at the University of Sharjah, UAE.
    Methods: A questionnaire-based survey was built based on the current literature and was distributed to all medical students at the University of Sharjah between February and March 2023. The questionnaire assessed the participants for their demographic data, access to and use of digital devices, exposure to and beliefs related to telemedicine, and their medical school experience with distance learning and telemedicine. The data were analyzed via simple statistics, and the Chi-square test was used to assess the associated factors affecting the participants' interest in receiving telemedicine teaching.
    Results: The questionnaire had a 70.4% (547/777) response rate. The mean age (SD) of the participants was 20.7 years (1.57), and the majority were female (68.4%). Over 98% of the students reported having easy access to and being comfortable with using computers and the internet. Most students (90.5%) believed that the medical school curriculum should include teaching in telemedicine; however, 78.2% of these students stated that it should be included as an elective course. The participants' interest in receiving teaching in telemedicine had a statistically significant association with the following factors: being female, being familiar with telemedicine, having read literature on telemedicine, having beliefs that telemedicine is an opportunity to improve current medical practice, that its use should be encouraged, that it has an important role to play in healthcare, that it does not pose greater threat to current medical practice, having a preference to continue distance learning at medical school and having an interest in incorporating telemedicine in their future careers.
    Conclusions: It is an ideal time to incorporate telemedicine into the medical curriculum at the University of Sharjah with most students expressing interest in it. However, further research is needed to assess its applicability to other medical schools in the country and elsewhere.
    MeSH term(s) Humans ; Male ; Female ; Young Adult ; Adult ; Students, Medical ; United Arab Emirates ; Curriculum ; Telemedicine ; Perception
    Language English
    Publishing date 2023-11-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 2044473-4
    ISSN 1472-6920 ; 1472-6920
    ISSN (online) 1472-6920
    ISSN 1472-6920
    DOI 10.1186/s12909-023-04859-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Domain Adaptation and Feature Fusion for the Detection of Abnormalities in X-Ray Forearm Images.

    Alzubaidi, Laith / Fadhel, Mohammed A / Albahri, A S / Salhi, Asma / Gupta, Ashish / Gu, YounTong

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2023  Volume 2023, Page(s) 1–5

    Abstract: The main challenge in adopting deep learning models is limited data for training, which can lead to poor generalization and a high risk of overfitting, particularly when detecting forearm abnormalities in X-ray images. Transfer learning from ImageNet is ... ...

    Abstract The main challenge in adopting deep learning models is limited data for training, which can lead to poor generalization and a high risk of overfitting, particularly when detecting forearm abnormalities in X-ray images. Transfer learning from ImageNet is commonly used to address these issues. However, this technique is ineffective for grayscale medical imaging because of a mismatch between the learned features. To mitigate this issue, we propose a domain adaptation deep TL approach that involves training six pre-trained ImageNet models on a large number of X-ray images from various body parts, then fine-tuning the models on a target dataset of forearm X-ray images. Furthermore, the feature fusion technique combines the extracted features with deep neural models to train machine learning classifiers. Gradient-based class activation heat map (Grad CAM) was used to verify the accuracy of our results. This method allows us to see which parts of an image the model uses to make its classification decisions. The statically results and Grad CAM have shown that the proposed TL approach is able to alleviate the domain mismatch problem and is more accurate in their decision-making compared to models that were trained using the ImageNet TL technique, achieving an accuracy of 90.7%, an F1-score of 90.6%, and a Cohen's kappa of 81.3%. These results indicate that the proposed approach effectively improved the performance of the employed models individually and with the fusion technique. It helped to reduce the domain mismatch between the source of TL and the target task.
    MeSH term(s) Deep Learning ; X-Rays ; Forearm/diagnostic imaging ; Machine Learning ; Radiography
    Language English
    Publishing date 2023-12-11
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC40787.2023.10340309
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article: Hospital selection framework for remote MCD patients based on fuzzy q-rung orthopair environment.

    Alamoodi, A H / Albahri, O S / Zaidan, A A / Alsattar, H A / Zaidan, B B / Albahri, A S

    Neural computing & applications

    2022  Volume 35, Issue 8, Page(s) 6185–6196

    Abstract: This research proposes a novel mobile health-based hospital selection framework for remote patients with multi-chronic diseases based on wearable body medical sensors that use the Internet of Things. The proposed framework uses two powerful multi- ... ...

    Abstract This research proposes a novel mobile health-based hospital selection framework for remote patients with multi-chronic diseases based on wearable body medical sensors that use the Internet of Things. The proposed framework uses two powerful multi-criteria decision-making (MCDM) methods, namely fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score method for criteria weighting and hospital ranking. The development of both methods is based on a Q-rung orthopair fuzzy environment to address the uncertainty issues associated with the case study in this research. The other MCDM issues of multiple criteria, various levels of significance and data variation are also addressed. The proposed framework comprises two main phases, namely identification and development. The first phase discusses the telemedicine architecture selected, patient dataset used and decision matrix integrated. The development phase discusses criteria weighting by q-ROFWZIC and hospital ranking by q-ROFDOSM and their sub-associated processes. Weighting results by q-ROFWZIC indicate that the time of arrival criterion is the most significant across all experimental scenarios with (
    Language English
    Publishing date 2022-11-17
    Publishing country England
    Document type Journal Article
    ZDB-ID 1480526-1
    ISSN 1433-3058 ; 0941-0643
    ISSN (online) 1433-3058
    ISSN 0941-0643
    DOI 10.1007/s00521-022-07998-5
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  10. Article ; Online: Development of hybrid feature learner model integrating FDOSM for golden subject identification in motor imagery.

    Al-Qaysi, Z T / Albahri, A S / Ahmed, M A / Mohammed, Saleh Mahdi

    Physical and engineering sciences in medicine

    2023  Volume 46, Issue 4, Page(s) 1519–1534

    Abstract: Brain-computer interfaces (BCIs) based on motor imagery (MI) face challenges due to the complex nature of brain activity, nonstationary and high-dimensional properties, and individual variations in motor behaviour. The identification of a consistent " ... ...

    Abstract Brain-computer interfaces (BCIs) based on motor imagery (MI) face challenges due to the complex nature of brain activity, nonstationary and high-dimensional properties, and individual variations in motor behaviour. The identification of a consistent "golden subject" in MI-based BCIs remains an open challenge, complicated by multiple evaluation metrics and conflicting trade-offs, presenting complex Multi-Criteria Decision Making (MCDM) problems. This study proposes a hybrid brain signal decoding model called Hybrid Adaboost Feature Learner (HAFL), which combines feature extraction and classification using VGG-19, STFT, and Adaboost classifier. The model is validated using a pre-recorded MI-EEG dataset from the BCI competition at Graz University. The fuzzy decision-making framework is integrated with HAFL to allocate a golden subject for MI-BCI applications through the Golden Subject Decision Matrix (GSDM) and the Fuzzy Decision by Opinion Score Method (FDOSM). The effectiveness of the HAFL model in addressing inter-subject variability in EEG-based MI-BCI is evaluated using an MI-EEG dataset involving nine subjects. Comparing subject performance fairly is challenging due to complexity variations, but the FDOSM method provides valuable insights. Through FDOSM-based External Group Aggregation (EGA), subject S5 achieves the highest score of 2.900, identified as the most promising golden subject for subject-to-subject transfer learning. The proposed methodology is compared against other benchmark studies from various key perspectives and exhibits significant novelty in several aspects. The findings contribute to the development of more robust and effective BCI systems, paving the way for advancements in subject-to-subject transfer learning for BCI-MI applications.
    MeSH term(s) Humans ; Electroencephalography/methods ; Imagination ; Imagery, Psychotherapy ; Brain-Computer Interfaces ; Learning
    Language English
    Publishing date 2023-08-21
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2662-4737
    ISSN (online) 2662-4737
    DOI 10.1007/s13246-023-01316-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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