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Abstract Pompe disease (OMIM #232300), a rare genetic disorder, leads to glycogen buildup in the body due to an enzyme deficiency, particularly harming the heart and muscles. Infantile-onset Pompe disease (IOPD) requires urgent treatment to prevent mortality, but the unavailability of these methods often delays diagnosis. Our study aims to streamline IOPD diagnosis in the UAE using electronic health records (EHRs) for faster, more accurate detection and timely treatment initiation. This study utilized electronic health records from the Abu Dhabi Healthcare Company (SEHA) healthcare network in the UAE to develop an expert rule-based screening approach operationalized through a dashboard. The study encompassed six diagnosed IOPD patients and screened 93,365 subjects. Expert rules were formulated to identify potential high-risk IOPD patients based on their age, particular symptoms, and creatine kinase levels. The proposed approach was evaluated using accuracy, sensitivity, and specificity. The proposed approach accurately identified five true positives, one false negative, and four false positive IOPD cases. The false negative case involved a patient with both Pompe disease and congenital heart disease. The focus on CHD led to the overlooking of Pompe disease, exacerbated by no measurement of creatine kinase. The false positive cases were diagnosed with Mitochondrial DNA depletion syndrome 12-A (SLC25A4 gene), Immunodeficiency-71 (ARPC1B mutation), Niemann–Pick disease type C (NPC1 gene mutation leading to frameshift), and Group B Streptococcus meningitis. The proposed approach of integrating expert rules with a dashboard facilitated efficient data visualization and automated patient screening, which aids in the early detection of Pompe disease. Future studies are encouraged to investigate the application of machine learning methodologies to enhance further the precision and efficiency of identifying patients with IOPD.

Body language refers to the unspoken communication conveyed through human body actions like body movements and postures, limb gestures, and facial and other bodily expressions. It acts as a transparent medium, exposing an individual’s emotions, attitudes, true thoughts, intentions, and physical and mental health states. A person may express pain using hand movements or other bodily cues, their facial expressions potentially offering insights into the intensity of the pain. Additionally, various diseases and pains can induce abnormalities in body movements, postures, and expressions, signaling distress or discomfort. Therefore, investigating the cause-effect relationships between diseases/pains and patients’ abnormal body language holds significant relevance, promising to enhance our understanding and management of these conditions. This importance has been reflected in numerous healthcare and artificial intelligence (AI) research articles. AI studies investigate this and related topics by detecting, recognizing, and analyzing patients’ abnormal activities and body actions using machine-learning techniques. However, most AI studies do not consider comprehensive domain knowledge that describes a complete and accurate list of patients’ abnormal actions caused by a disease or pain. Though these results appear consistent and stable from an AI outlook, they fall short when viewed through the prism of healthcare, primarily because the limited domain knowledge incorporated in the AI studies makes the findings partially incomplete. To overcome these drawbacks, this paper comprehensively reviews healthcare and medical studies centered on patients’ body language from an AI outlook. It presents a thorough descriptive and exploratory analysis of the findings, yielding a more accurate and comprehensive understanding of the causational connections between diseases and abnormal body actions and the strength of the evidence supporting these connections. The analysis enables us to define “disease-to-abnormality” and “abnormality-to-disease” mappings that result in building exhaustive and accurate lists of abnormal body actions induced by diseases and pains as well as lists of diseases and pains causing particular abnormal body actions. The generation of these lists is assisted by the concepts of “correlation strength index” and “strongly correlated selection” defined in this paper. The paper’s results have significant implications for developing machine learning systems that can more accurately analyze patients’ physical and mental health states, correctly identify external signs and symptoms of diseases, and effectively monitor health conditions.

Body language is a nonverbal communication process consisting of movements, postures, gestures, and expressions of the body or body parts. Body language expresses human feelings, thoughts, and intentions. It also reveals physical and psychological health conditions: abnormal activities inform peoples’ health conditions, facial expressions indicate their emotional states and abnormal body actions convey specific diseases’ external signs and symptoms. We can observe the importance of studying the body language of people with health conditions through many reports in literature written by healthcare (medical) and artificial intelligence researchers. This paper comprehensively reviews artificial intelligence-based articles that have studied patients’ body language. We also conduct different descriptive and exploratory examinations of the findings using data analysis techniques, which provide more authentic domain knowledge of abnormal activities, abnormal body actions, and more precise analysis of methodologies used in machine learning tasks for studying these abnormalities. The paper’s results are essential for developing intelligent automated systems that accurately evaluate patients’ physical and psychological conditions, precisely identify external signs and symptoms of diseases, and adequately monitor patients’ health conditions.

This paper presents an automatic visual inspection of exterior surface defects of oil tanks using unmanned aerial vehicles (UAVs) and image processing with two cascading fuzzy logic algorithms. Corrosion is one of the defects that has a serious effect on the safety of the surface of oil and gas tanks. At present, human inspection, and climbing robots inspection are the dominant approach for rust detection in oil and gas tanks. However, there are many shortcomings to this approach, such as taking longer, high cost, and covering less surface area inspection of the tank. The purpose of this research is to detect the rust in oil tanks by localizing visual inspection technology using UAVs, as well as to develop algorithms to distinguish between defects and noise. The study focuses on two basic aspects of oil tank inspection through the images captured by the UAV, namely, the detection of defects and the distinction between defects and noise. For the former, an image processing algorithm was developed to improve or remove noise, adjust the brightness of the captured image, and extract features to identify defects in oil tanks. Meanwhile, for the latter aspect, a cascading fuzzy logic algorithm and threshold algorithm were developed to distinguish between defects and noise levels and reduce their impact through three stages of processing: The first stage of fuzzy logic aims to distinguish between defects and low noise generated by the appearance of objects on the surface of the tank, such as trees or stairs, and reduce their impact. The second stage aims to distinguish between defects and medium noise generated by shadows or the presence of small objects on the surface of the tank and reduce their impact. The third stage of the thresholding algorithm aims to distinguish between defects and high noise generated by sedimentation on the surface of the tank and reduce its impact. The samples were classified based on the output of the third stage of the threshold process into defective or non-defective samples. The proposed algorithms were tested on 180 samples and the results show its superiority in the inspection and detection of defects with an accuracy of 83%.