[Retracted] A Survey Research on the Willingness of Gynecologists to Employ Mobile Health Applications

Alazzam, Malik Bader; Al-Radaideh, Ahmad Tawfig; Alhamarnah, Raed Ahmed; Alassery, Fawaz; Hajjej, Fahima; Halasa, Azmi

doi:10.1155/2021/1220374

Cited by 27 publications

(24 citation statements)

References 24 publications

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“…According to our knowledge, this type of localizer has not been used before to localize PMC. Previous rt-fMRI research targeted PMC and used techniques to localize PMC, such as a PMC mask based on an Atlas [ 31 , 32 ], motor execution [ 33 ], anatomy reference, or action observation [ 34 ]. The bilateral ventral premotor cortex and dorsal premotor cortex have all been consistently implicated in motor imagery and action observation [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

Functional Localizers for Motor Areas of the Brain Using fMRI

Madkhali

Aldehmi

Pollick

2022

Computational Intelligence and Neuroscience

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Neuroimaging researchers increasingly take advantage of the known functional properties of brain regions to localize motor regions in the brain and investigate changes in their activity under various conditions. Using this noninvasive functional MRI (fMRI) method makes it possible to identify and localize brain activation. There are many localizers that can be used to identify brain areas, namely, motor areas such as functional localizer, anatomical localizer, or Atlas mask. Eighteen right-handed participants were recruited for this research to test the reliability of five localizers for primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PMC), motor cerebellum, and motor thalamus. Motor execution task, namely, hand clenching was used to activate M1, SMA, and motor cerebellum. A combined action observation and motor imagery (AOMI) task was used to functionally activate PMC. Finally, a mask based on Talairach coordinates Atlas was created and used to identify the motor thalamus. Our results show that all localizers were successfully activated in the desired regions of interest. Motor execution successfully activated M1, SMA, and motor cerebellum. A novel localizer based on AOMI was successfully activated in PMC, and the motor thalamus mask obtained from the thalamus mask was successfully implemented on each participant. In conclusion, all five localizers tested in this research were reliable and can be used for rt-fMRI neurofeedback research to define the regions of interest.

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Section: Discussionmentioning

confidence: 99%

Functional Localizers for Motor Areas of the Brain Using fMRI

Madkhali

Aldehmi

Pollick

2022

Computational Intelligence and Neuroscience

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“…46.8% of the patients appeared to have poor awareness levels, 29.2% were moderate and only 24% were shown to have a good understanding of the disease. Several studies established a low level of awareness in patients and their caregivers [1,7,[15][16][17][18][19][20]. However, in Palestine [19], hypertensive individuals scored higher on total knowledge and attitude toward chronic renal disease prevention and early detection.…”

Section: Discussionmentioning

confidence: 99%

[Retracted] Measuring the Awareness of Chronic Kidney Disease (CKD) with Environmental Evaluation among Adult Diabetic Patients in Hail Region, Saudi Arabia

Alreshidi

Alkhateeb

Alsharari

et al. 2022

Journal of Environmental and Public Health

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Introduction. Chronic kidney disease (CKD) is one of the main chronic complications of T2DM that happens among T2DM patients who have uncontrolled glucose. Because CKD is considered a silent disease, the diagnosis is usually made at late stages when there will be few chances to prevent the adverse outcome. Aim. The goal of this study was to assess adult diabetic patients’ awareness of developing chronic kidney disease at the community level in Hail region, Saudi Arabia, in 2022. Patients and Methods. This is a cross-sectional study conducted among diabetic patients in the Hail region, Saudi Arabia. A self-administered questionnaire translated into Arabic was distributed among patients with DM. The questionnaire covers social and demographic variables (such as age, gender, relationship status, and so on) as well as a 7-item questionnaire to assess the DM population’s knowledge of CKD. Results. 400 DM patients responded to our survey (51% females vs 49% males). Patients who were diagnosed with type 2 diabetes were 23.8% and 40.5% had a diabetes duration of 5–15 years. Nearly half (46.8%) were considered as a poor level of awareness, 29.3% had a moderate, and 24% had a good awareness level. Factors associated with an increased level of awareness were being a bachelor’s degree, being unmarried, being a student, and having a doctor as a source of CKD information. Conclusion. There was a deficiency in the level of awareness among the diabetic patients in our region. Patients who were single with better education and who were well informed by the doctors about CKD information tend to be more aware of CKD as compared to other DM patients. Further research is warranted in order to establish the awareness level of DM patients regarding CKD and its complications.

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“…In this section, we will determine the values of the model hyperparameters such as the learning rate ǫ, the regularisation weight λ, the weight of the positive class p, the batch size, and the number of learning epochs. A grid search similar to that of [20] was performed. e values obtained were: ǫ of 10 −3 , λ of 10 −5 , and p of 2500. e number of learning epochs was set to 300 epochs, and the batch size was modified on a per-experiment basis due to GPU memory constraints.…”

Section: Hyperparametersmentioning

confidence: 99%

“…This proposal outperformed the models offered by 2 mm, which is regarded as acceptable in dentistry. Juan Ignacio Porta [ 20 ] proposed a new architecture that uses a CNN with Inception layers and an Autoencoder to assign a probability map to an image input. The goal is to generate Bjork–Jarabak and Ricketts cephalometrics automatically.…”

Section: Introductionmentioning

confidence: 99%

Automation of Cephalometrics Using Machine Learning Methods

Alshamrani

Alqahtani

Alshehri

2022

Computational Intelligence and Neuroscience

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Cephalometry is a medical test that can detect teeth, skeleton, or appearance problems. In this scenario, the patient’s lateral radiograph of the face was utilised to construct a tracing from the tracing of lines on the lateral radiograph of the face of the soft and hard structures (skin and bone, respectively). Certain cephalometric locations and characteristic lines and angles are indicated after the tracing is completed to do the real examination. In this unique study, it is proposed that machine learning models be employed to create cephalometry. These models can recognise cephalometric locations in X-ray images, allowing the study’s computing procedure to be completed faster. To correlate a probability map with an input image, they combine an Autoencoder architecture with convolutional neural networks and Inception layers. These innovative architectures were demonstrated. When many models were compared, it was observed that they all performed admirably in this task.

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[Retracted] A Survey Research on the Willingness of Gynecologists to Employ Mobile Health Applications

Cited by 27 publications

References 24 publications

Functional Localizers for Motor Areas of the Brain Using fMRI

Functional Localizers for Motor Areas of the Brain Using fMRI

[Retracted] Measuring the Awareness of Chronic Kidney Disease (CKD) with Environmental Evaluation among Adult Diabetic Patients in Hail Region, Saudi Arabia

Automation of Cephalometrics Using Machine Learning Methods

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