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Introduction: Despite the many attempts made by researchers to diagnose jaundice non-invasively using machine learning techniques, the low amount of data used to build their models remains the key factor limiting the performance of their models. Objective: To build a system to diagnose neonatal jaundice non-invasively based on machine learning algorithms created based on a dataset comprising 767 infant images using a computer device and a USB webcam. Methods: The first stage of the proposed system was to evaluate the performance of four machine learning algorithms, namely support vector machine (SVM), k nearest neighbor (k-NN), random forest (RF), and extreme gradient boost (XGBoost), based on a dataset of 767 infant images. The algorithm with the best performance was chosen as the classifying algorithm in the developed application. The second stage included designing an application that enables the user to perform jaundice detection for a patient under test with the minimum effort required by capturing the patient’s image using a USB webcam. Results: The obtained results of the first stage of the machine learning algorithms evaluation process indicated that XGBoost outperformed the rest of the algorithms by obtaining an accuracy of 99.63%. The second-best algorithm was the RF algorithm, which had an accuracy of 98.99%. Following RF, with a slight difference, was the k-NN algorithm. It achieved an accuracy of 98.25%. SVM scored the lowest performance among the above three algorithms, with an accuracy of 96.22%. Based on these obtained results, the XGBoost algorithm was chosen to be the classifier of the proposed system. In the second stage, the jaundice application was designed based on the model created by the XGBoost algorithm. This application ensured it was user friendly with as fast a processing time as possible. Conclusion: Early detection of neonatal jaundice is crucial due to the severity of its complications. A non-invasive system using a USB webcam and an XGBoost machine learning technique was proposed. The XGBoost algorithm achieved 99.63% accuracy and successfully diagnosed 10 out of 10 NICU infants with very little processing time. This denotes the efficiency of machine learning algorithms in healthcare in general and in monitoring systems specifically.

Neonatal jaundice is a prevalent condition among newborns, with potentially severe complications that can result in permanent brain damage if left untreated during its early stages. The existing approaches for jaundice detection involve invasive procedures such as blood sample collection, which can inflict pain and distress on the patient, and may give rise to additional complications. Alternatively, a non-invasive method using image-processing techniques and implementing kNN, Random Forest, and XGBoost machine learning algorithms as a classifier can be employed to diagnose jaundice, necessitating a comprehensive database of infant images to achieve a diagnosis with high accuracy. This data article presents the NJN collection, a repository of newborn images encompassing diverse birthweights and skin tones, spanning an age range of 2 to 8 days. The dataset is accompanied by an Excel sheet file in CSV format containing the RGB and YCrCb channel values, as well as the status of each sample. The dataset and associated resources are openly accessible at Zenodo website. Moreover, the Python code for data testing utilizing various AI techniques is provided. Consequently, this article offers an unparalleled resource for AI researchers, enabling them to train their AI systems and develop algorithms that can assist neonatal intensive care unit (NICU) healthcare specialists in monitoring neonates while facilitating the fast, real-time, non-invasive, and accurate diagnosis of jaundice.

Sheehan’s syndrome is a postpartum hypopituitarism state caused by necrosis of the pituitary gland. It is usually the result of severe hypotension or shock caused by massive hemorrhage during or after delivery. Sheehan’s syndrome symptoms are often subtle and hence it is diagnosed late. Herein, we report a case of a 41-year-old woman who developed severe postpartum hemorrhage after childbirth that required a total abdominal hysterectomy to control bleeding at the age of 36 years. Since then, she has progressively developed symptoms of headache, general fatigue, and malaise, and finally presented with pancytopenia for investigations. Anemia is a well-known hematological association with Sheehan’s syndrome while pancytopenia is rarely reported. However, complete recovery of pancytopenia was observed after the treatment. Pancytopenia (due to bone marrow failure to produce cells) is a serious finding in clinical practice that causes significant stress as it may point to a diagnosis of malignancy (mainly leukemia) and other serious disorders. Despite being a rare cause, a high index of suspicion is required from the physicians in women with pancytopenia, in order to look for a possible treatable cause of pancytopenia (like Sheehan's syndrome), if the common causes were excluded.

Introduction: Despite the many attempts made by researchers to diagnose jaundice non-invasively using machine learning techniques, the low amount of data used to build their models remains the key factor limiting the performance of their models. Objective: To build a system to diagnose neonatal jaundice non-invasively based on machine learning algorithms created based on a dataset comprising 767 infant images using a computer device and a USB webcam. Methods: The first stage of the proposed system was to evaluate the performance of four machine learning algorithms, namely support vector machine (SVM), k nearest neighbor (k-NN), random forest (RF), and extreme gradient boost (XGBoost), based on a dataset of 767 infant images. The algorithm with the best performance was chosen as the classifying algorithm in the developed application. The second stage included designing an application that enables the user to perform jaundice detection for a patient under test with the minimum effort required by capturing the patient's image using a USB webcam. Results: The obtained results of the first stage of the machine learning algorithms evaluation process indicated that XGBoost outperformed the rest of the algorithms by obtaining an accuracy of 99.63%. The second-best algorithm was the RF algorithm, which had an accuracy of 98.99%. Following RF, with a slight difference, was the k-NN algorithm. It achieved an accuracy of 98.25%. SVM scored the lowest performance among the above three algorithms, with an accuracy of 96.22%. Based on these obtained results, the XGBoost algorithm was chosen to be the classifier of the proposed system. In the second stage, the jaundice application was designed based on the model created by the XGBoost algorithm. This application ensured it was user friendly with as fast a processing time as possible. Conclusion: Early detection of neonatal jaundice is crucial due to the severity of its complications. A non-invasive system using a USB webcam and an XGBoost machine learning technique was proposed. The XGBoost algorithm achieved 99.63% accuracy and successfully diagnosed 10 out of 10 NICU infants with very little processing time. This denotes the efficiency of machine learning algorithms in healthcare in general and in monitoring systems specifically. [ABSTRACT FROM AUTHOR]

Background. β-Thalassemia is a common inherited hemolytic disorder in Palestine. Red blood cell (RBC) transfusion is the principal treatment but it may cause RBC alloimmunization. This study was conducted to determine the prevalence and characteristics of RBC alloimmunization among thalassemic patients in northern governorates of Palestine. Methods. A prospective multicenter observational study was conducted in the thalassemia transfusion centers in the northern governorates of Palestine. The study included 215 thalassemia patients who received regular blood transfusions. Clinical and transfusion records of patients were examined. Antibody screening and identification was conducted using the microcolum gel technique. Results. Two hundred fifteen patients were included in the study. More than half (52.1%) of the patients were males. The median age of patients was 18 years (range: 12–24 years). The most frequent blood group was A (40.5%). Alloantibodies were detected in 12.6% of patients. Anti-D (33.3%), anti-K (25.9%) and anti-E (14.8%) were the most commonly isolated antibodies. There was no association between age, sex, starting age of transfusion, number of transfused units, history of splenectomy and alloimmunization. Conclusions. Anti-Rh and anti-K antibodies were common among this cohort of patients. Age, sex, starting age of transfusion, number of transfused units, and history of splenectomy could not predict the occurrence of alloimmunization.

Neonatal jaundice is a prevalent condition among newborns, with potentially severe complications that can result in permanent brain damage if left untreated during its early stages. The existing approaches for jaundice detection involve invasive procedures such as blood sample collection, which can inflict pain and distress on the patient, and may give rise to additional complications. Alternatively, a non-invasive method using image-processing techniques and implementing kNN, Random Forest, and XGBoost machine learning algorithms as a classifier can be employed to diagnose jaundice, necessitating a comprehensive database of infant images to achieve a diagnosis with high accuracy. This data article presents the NJN collection, a repository of newborn images encompassing diverse birthweights and skin tones, spanning an age range of 2 to 8 days. The dataset is accompanied by an Excel sheet file in CSV format containing the RGB and YCrCb channel values, as well as the status of each sample. The dataset and associated resources are openly accessible at Zenodo website. Moreover, the Python code for data testing utilizing various AI techniques is provided. Consequently, this article offers an unparalleled resource for AI researchers, enabling them to train their AI systems and develop algorithms that can assist neonatal intensive care unit (NICU) healthcare specialists in monitoring neonates while facilitating the fast, real-time, non-invasive, and accurate diagnosis of jaundice. [ABSTRACT FROM AUTHOR]

At the beginning of their lives, newborns may have a widespread condition known as Jaundice or Hyperbilirubinemia. High levels of bilirubin in the blood are the primary cause of jaundice. Severe cases of jaundice may cause acute bilirubin encephalopathy due to the toxicity of bilirubin to the cells of the brain, which may lead to kernicterus. Kernicterus causes several symptoms, including a permanent upward look, loss of hearing, and repetitive and uncontrolled movements. Therefore, diagnosing this condition at the appropriate time helps to prevent chronic effects. In this study, jaundice or hyperbilirubinemia is diagnosed using a computer vision system based on a random forest algorithm. The system comprises a digital HD camera, a computer device with a Matlab application installed to analyze and detect the skin color changes of the infant, and an Arduino Uno microcontroller to control an LED ultraviolet light. A set of neonate images were collected to train the random forest algorithm, including 374 for normal and 137 for jaundiced infants. |The experimental results using the random forest algorithm for classification reached an accuracy of 98.4375%. The results of this study are promising and open doors for new monitoring applications in various medical diseases detection with a high degree of accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the topological properties of the image under the mapping between two topological spaces. We show then image mapping is almost topological space with certain condition subset of a given space. The space is called normal regular and normal regular compact space. Then we show that every regular compact space and for any two disjoint regularly closed subsets. is almost mildly normal compact regular space. [ABSTRACT FROM AUTHOR]

Neodymium vanadate with concentration of gadolinium as 3% and 5% having general formulae GdxNd1-xVO4 where x = 0.03 and 0.05 was prepared by chemical route technique. The effect of concentration of gadolinium on the structural, optical and spectroscopic properties was explained by various characterization techniques. Powder X-ray diffraction study suggests tetragonal zircon structure; various parameters such as crystallite size, volume and X-ray density were calculated. A change in morphology with the change in concentration of gadolinium was observed through scanning electron microscopy. Ultraviolet absorption spectroscopy shows various transitions from ground state to various excited state within 4f shell, and variation in the optical band gap calculated by Tauc plot is due to the incorporation of Gd3+ ions. [ABSTRACT FROM AUTHOR]