RETRACTED: Hosni Mahmoud, H.A.; Alabdulkreem, E. Bidirectional Neural Network Model for Glaucoma Progression Prediction. J. Pers. Med. 2023, 13, 390.

The Journal of Personalized Medicine retracts the article Bidirectional Neural Network Model for Glaucoma Progression Prediction [...].

Bidirectional Neural Network Model for Glaucoma Progression Prediction.

Deep learning models are usually utilized to learn from spatial data, only a few studies are proposed to predict glaucoma time progression utilizing deep learning models. In this article, we present a bidirectional recurrent deep learning model (Bi-RM) to detect prospective progressive visual field diagnoses. A dataset of 5413 different eyes from 3321 samples is utilized as the learning phase dataset and 1272 eyes are used for testing. Five consecutive diagnoses are recorded from the dataset as input and the sixth progressive visual field diagnosis is matched with the prediction of the Bi-RM. The precision metrics of the Bi-RM are validated in association with the linear regression algorithm (LR) and term memory (TM) technique. The total prediction error of the Bi-RM is significantly less than those of LR and TM. In the class prediction, Bi-RM depicts the least prediction error in all three methods in most of the testing cases. In addition, Bi-RM is not impacted by the reliability keys and the glaucoma degree.

Intelligent Monitoring Model for Fall Risks of Hospitalized Elderly Patients.

Early detection of high fall risk is an important process of fall prevention in hospitalized elderly patients. Hospitalized elderly patients can face several falling risks. Monitoring systems can be utilized to protect health and lives, and monitoring models can be less effective if the alarm is not invoked in real time. Therefore, in this paper we propose a monitoring prediction system that incorporates artificial intelligence. The proposed system utilizes a scalable clustering technique, namely the Catboost method, for binary classification. These techniques are executed on the Snowflake platform to rapidly predict safe and risky incidence for hospitalized elderly patients. A later stage employs a deep learning model (DNN) that is based on a convolutional neural network (CNN). Risky incidences are further classified into various monitoring alert types (falls, falls with broken bones, falls that lead to death). At this phase, the model employs adaptive sampling techniques to elucidate the unbalanced overfitting in the datasets. A performance study utilizes the benchmarks datasets, namely SERV-112 and SV-S2017 of the image sequences for assessing accuracy. The simulation depicts that the system has higher true positive counts in case of all health-related risk incidences. The proposed system depicts real-time classification speed with lower training time. The performance of the proposed multi-risk prediction is high at 87.4% in the SERV-112 dataset and 98.71% in the SV-S2017 dataset.

A Deep Learning Model for Stroke Patients' Motor Function Prediction.

Deep learning models are effectively employed to transfer learning to adopt learning from other areas. This research utilizes several neural structures to interpret the electroencephalogram images (EEG) of brain-injured cases to plan operative imagery-computerized interface models for controlling left and right hand movements. This research proposed a model parameter tuning with less training time using transfer learning techniques. The precision of the proposed model is assessed by the aptitudes of motor imagery detection. The experiments depict that the best performance is attained with the incorporation of the proposed EEG-DenseNet and the transfer model. The prediction accuracy of the model reached 96.5% with reduced time computational cost. These high performance proves that the EEG-DenseNet model has high prospective for motor imagery brain-injured therapy systems. It also productively exhibited the effectiveness of transfer learning techniques for enhancing the accuracy of electroencephalogram brain-injured therapy models.

The Prediction of Consumer Behavior from Social Media Activities.

Consumer behavior variants are evolving by utilizing advanced packing models. These models can make consumer behavior detection considerably problematic. New techniques that are superior to customary models to be utilized to efficiently observe consumer behaviors. Machine learning models are no longer efficient in identifying complex consumer behavior variants. Deep learning models can be a capable solution for detecting all consumer behavior variants. In this paper, we are proposing a new deep learning model to classify consumer behavior variants using an ensemble architecture. The new model incorporates two pretrained learning algorithms in an optimized fashion. This model has four main phases, namely, data gathering, deep neural modeling, model training, and deep learning model evaluation. The ensemble model is tested on Facemg BIG-D15 and TwitD databases. The experiment results depict that the ensemble model can efficiently classify consumer behavior with high precision that outperforms recent models in the literature. The ensemble model achieved 98.78% accuracy on the Facemg database, which is higher than most machine learning consumer behavior detection models by more than 8%.

A Decoding Prediction Model of Flexion and Extension of Left and Right Feet from Electroencephalogram.

Detection of limb motor functions utilizing brain signals is a significant technique in the brain signal gain model (BSM) that can be effectively employed in various biomedical applications. Our research presents a novel technique for prediction of feet motor functions by applying a deep learning model with cascading transfer learning technique to use the electroencephalogram (EEG) in the training stage. Our research deduces the electroencephalogram data (EEG) of stroke incidence to propose functioning high-tech interfaces for predicting left and right foot motor functions. This paper presents a transfer learning with several source input domains to serve a target domain with small input size. Transfer learning can reduce the learning curve effectively. The correctness of the presented model is evaluated by the abilities of motor functions in the detection of left and right feet. Extensive experiments were performed and proved that a higher accuracy was reached by the introduced BSM-EEG neural network with transfer learning. The prediction of the model accomplished 97.5% with less CPU time. These accurate results confirm that the BSM-EEG neural model has the ability to predict motor functions for brain-injured stroke therapy.

Pneumonia Transfer Learning Deep Learning Model from Segmented X-rays.

Pneumonia is a common disease that occurs in many countries, more specifically, in poor countries. This disease is an obstructive pneumonia which has the same impression on pulmonary radiographs as other pulmonary diseases, which makes it hard to distinguish even for medical radiologists. Lately, image processing and deep learning models are established to rapidly and precisely diagnose pneumonia disease. In this research, we have predicted pneumonia diseases dependably from the X-ray images, employing image segmentation and machine learning models. A public labelled database is utilized with 4000 pneumonia disease X-rays and 4000 healthy X-rays. ImgNet and SqueezeNet are utilized for transfer learning from their previous computed weights. The proposed deep learning models are trained for classifying pneumonia and non-pneumonia cases. The following processes are presented in this paper: X-ray segmentation utilizing BoxENet architecture, X-ray classification utilizing the segmented chest images. We propose the improved BoxENet model by incorporating transfer learning from both ImgNet and SqueezeNet using a majority fusion model. Performance metrics such as accuracy, specificity, sensitivity and Dice are evaluated. The proposed Improved BoxENet model outperforms the other models in binary and multi-classification models. Additionally, the Improved BoxENet has higher speed compared to other models in both training and classification.

Effect of maternal BMI on labor outcomes in primigravida pregnant women.

BACKGROUND: This study aims to detect the effects of increased BMI on labor outcomes in primigravida pregnant women. METHODS: A cross-sectional study involved 600 full-term singleton primigravida pregnant women who presented in the active phase of labor to the labor ward. They were divided according to BMI into three equals groups; women with normal BMI (group I), overweight women (group II), and women with class I obesity (group III). RESULTS: We found that high BMI was associated with a significantly increased risk of Caesarean section (C.S.) (13% in group I, 18% in group II and 40% in group III). Women with higher BMI and delivered vaginally had a significantly prolonged first and second stage of labor, consequently increased the need for oxytocin augmentation as well as the oxytocin dose. Regarding the maternal and fetal outcomes, there are significantly increased risks of postpartum sepsis, perineal tears, wound infection, as well as significantly increased birth weight and longer neonatal stay in the neonatal unit (NNU). CONCLUSION: Obese primigravida pregnant women were at higher risk of suboptimal outcomes. Besides, prolonged first and second stages of labor and the incidence of C.S. have also been increased.

Role of suppression of endometriosis with progestins before IVF-ET: a non-inferiority randomized controlled trial.

BACKGROUND: Endometriosis affects the responsiveness to ovarian stimulation. This study aimed to assess the role of Dienogest pretreatment for endometriosis suppression as compared to Gonadotropin-releasing hormone agonist (GnRHa) in patients with endometriosis pursuing IVF treatment. METHODS: In this randomized controlled trial, 134 women with endometriosis-related infertility were randomly allocated to group A (n = 67) who had monthly depot GnRHa for 3 months before ovarian stimulation in IVF treatment (Ultra-long protocol), and Group B (n = 67) who had daily oral Dienogest 2 mg/d for 3 months before starting standard long protocol for IVF. The primary outcome measure was the number of oocytes retrieved. The secondary outcome measures included the number of mature oocytes, fertilization rate, quality of life assessed by FertiQoL scores, cost of treatment, and pregnancy outcomes. RESULTS: Although there was no statistically significant difference between both groups regarding ovarian stimulation, response parameters, and pregnancy outcomes, the Dienogest group had a lower cost of treatment (2773 vs. 3664 EGP, P < 0.001), lower side effects (29.9% vs. 59.7%, P < 0.001), higher FertiQoL treatment scores (33.2 vs. 25.1, P < 0.001) and higher tolerability scores (14.1 vs. 9.4, P < 0.001 < 0.001). CONCLUSION: Our study indicates that Dienogest is a suitable and safe substitute for GnRHa pretreatment in endometriosis patients. TRIAL REGISTRATION: NCT04500743 "Retrospectively registered on August 5, 2020".

A novel technique for automated concealed face detection in surveillance videos.

Face detection perceives great importance in surveillance paradigm and security paradigm areas. Face recognition is the technique to identify a person identity after face detection. Extensive research has been done on these topics. Another important research problem is to detect concealed faces, especially in high-security places like airports or crowded places like concerts and shopping centres, for they may prevail security threat. Also, in order to help effectively in preventing the spread of Coronavirus, people should wear masks during the pandemic especially in the entrance to hospitals and medical facilities. Surveillance systems in medical facilities should issue warnings against unmasked people. This paper presents a novel technique for concealed face detection based on complexion detection to challenge a concealed face assumption. The proposed algorithm first determine of the existence of a human being in the surveillance scene. Head and shoulder contour will be detected. The face will be clustered to cluster patches. Then determination of presence or absent of human skin will be determined. We proposed a hybrid approach that combines normalized RGB (rgb) and the YCbCr space color. This technique is tested on two datasets; the first one contains 650 images of skin patches. The second dataset contains 800 face images. The algorithm achieves an average detection rate of 97.51% for concealed faces. Also, it achieved a run time comparable with existing state-of-the-art concealed face detection systems that run in real time.

Role of 3-D Transvaginal Ultrasonography in Women Undergoing in Vitro Fertilization/Intra-cytoplasmic Sperm Injection.

Both 2-D and 3-D transvaginal ultrasonography are effective imaging modalities for assessment of ovarian reserve. Our aim was to compare both modalities in assessment of ovarian reserve of women undergoing in vitro fertilization/intra-cytoplasmic sperm injection (IVF/ICSI). Fifty women were scheduled according to their menstrual cycle to be examined by both 2-D and 3-D transvaginal ultrasonography. We found that the average time for computerized analysis of the 3-D ultrasound data was significantly shorter than that for analysis of the 2-D ultrasound data, for both total antral follicle count and ovarian volume. However, there were no statistically significant differences between the methods in total antral follicle count and ovarian volume. We conclude that, where available, 3-D ultrasonography can be used for assessment of ovarian reserve in addition to the biochemical marker, particularly in overcrowded in vitro fertilization centers that need to save time.