Development of a CNN for Adult Brain Tumour Characterisation: Implications and Future Directions for Transfer Learning.

Brain tumours are the most commonly occurring solid tumours in children, albeit with lower incidence rates compared to adults. However, their inherent heterogeneity, ethical considerations regarding paediatric patients, and difficulty in long-term follow-up make it challenging to gather large homogenous datasets for analysis. This study focuses on the development of a Convolutional Neural Network (CNN) for brain tumour characterisation using the adult BraTS 2020 dataset. We propose to transfer knowledge, from models pre-trained on extensive adult brain tumour datasets to smaller cohort datasets (e.g., paediatric brain tumours) in future studies, by leveraging Transfer Learning (TL). This approach aims to extract relevant features from pre-trained models, addressing the limited availability of annotated paediatric datasets and enhancing tumour characterisation in children. The implications and potential applications of this methodology in paediatric neuro-oncology are discussed.

Deep Learning-Based Synthetic Skin Lesion Image Classification.

Advances in general-purpose computers have enabled the generation of high-quality synthetic medical images that human eyes cannot differ between real and AI-generated images. To analyse the efficacy of the generated medical images, this study proposed a modified VGG16-based algorithm to recognise AI-generated medical images. Initially, 10,000 synthetic medical skin lesion images were generated using a Generative Adversarial Network (GAN), providing a set of images for comparison to real images. Then, an enhanced VGG16-based algorithm has been developed to classify real images vs AI-generated images. Following hyperparameters tuning and training, the optimal approach can classify the images with 99.82% accuracy. Multiple other evaluations have been used to evaluate the efficacy of the proposed network. The complete dataset used in this study is available online to the research community for future research.

Clinical Decision Support Systems for Brain Tumour Diagnosis and Prognosis: A Systematic Review.

CDSSs are being continuously developed and integrated into routine clinical practice as they assist clinicians and radiologists in dealing with an enormous amount of medical data, reduce clinical errors, and improve diagnostic capabilities. They assist detection, classification, and grading of brain tumours as well as alert physicians of treatment change plans. The aim of this systematic review is to identify various CDSSs that are used in brain tumour diagnosis and prognosis and rely on data captured by any imaging modality. Based on the 2020 preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, the literature search was conducted in PubMed and Engineering Village Compendex databases. Different types of CDSSs identified through this review include Curiam BT, FASMA, MIROR, HealthAgents, and INTERPRET, among others. This review also examines various CDSS tool types, system features, techniques, accuracy, and outcomes, to provide the latest evidence available in the field of neuro-oncology. An overview of such CDSSs used to support clinical decision-making in the management and treatment of brain tumours, along with their benefits, challenges, and future perspectives has been provided. Although a CDSS improves diagnostic capabilities and healthcare delivery, there is lack of specific evidence to support these claims. The absence of empirical data slows down both user acceptance and evaluation of the actual impact of CDSS on brain tumour management. Instead of emphasizing the advantages of implementing CDSS, it is important to address its potential drawbacks and ethical implications. By doing so, it can promote the responsible use of CDSS and facilitate its faster adoption in clinical settings.

A multi-centre survey on hospital malnutrition: result of PNSI study.

BACKGROUND: Disease-related malnutrition is associated with adverse outcomes such as increased rates of morbidity and mortality, prolonged hospital stay, and extra costs of health care. This study was conducted to assess nutritional status among patients and to determine the risk factors for malnutrition in Iran university f. METHODS: Persian Nutritional Survey In Hospitals (PNSI) was a cross-sectional study that conducted in 20 university hospitals across Iran. All the patients with age range of 18 to 65 years, who were admitted or discharged, were assessed by subjective global assessment (SGA). RESULTS: In total, 2109 patients were evaluated for malnutrition. Mean values of age and body mass index were 44.68 ± 14.65 years and 25.44 ± 6.25 kg/m2, respectively. Malnutrition (SGA-B & C) was identified in 23.92% of the patients, 26.23 and 21% of whom were among the admitted and discharged patients, respectively. The highest prevalence of malnutrition was in burns (77.70%) and heart surgery (57.84%) patients. Multivariate analysis presented male gender (OR = 1.02, P < 0.00), malignant disease (OR = 1.40, P < 0.00), length of hospital stay (OR = 1.20, P < 0.00), and polypharmacy (OR = 1.06, P < 0.00) as independent risk factors for malnutrition. Malnutrition was not associated with age (P = 0.10). CONCLUSION: This study provides an overall and comprehensive illustration of hospital malnutrition in Iran university hospitals, finding that one out of four patients were malnourished; thus, appropriate consideration and measures should be taken to this issue.

Nutritional status of patients hospitalized in the intensive care unit: A comprehensive report from Iranian hospitals, 2018.

INTRODUCTION AND AIM: Malnutrition is a complication of hospitalization in critically ill patients. This event is occurred because of disease and therapeutic processes for curing the patients. Determination of nutritional status helps physicians and clinical nutritionists decide on the best regimen which should be prescribed for a patient. In the current study, we aimed to report the nutritional status ofpatientshospitalizedin the intensive care unit (ICU). METHOD OF STUDY: We used three standard tolls, including Subjective global assessment (SGA), Nutrition Risk in the Critically Ill (NUTRIC) Score and nutrition risk screening (NRS) questionnaires via a multi-stage sampling for different ICU wards of 32 university hospitals in Iran. Frequencies and rates of nutritional scores, comparative studies, and determined agreement of scoring systems and nutritional status in any ward of hospitals were evaluated. RESULTS: There were 771 males and 540 female Cancer and trauma patients had the best and worst nutritional scores, respectively. Using NRS and NUTRIC, the low-risk scores were more frequent than thehigh-riskscores among ICU patients. SGA showed that most patients were in grades A (well nutritional status) or B (moderate nutritional status), andfew caseswere in grade C (poor nutritional status).The high-risk nutritional score wasobtained for older patients. NUTRIC and NRS had better agreement for diagnosis and differentiation of malnutrition than NUTRIC-SGA or NRS-SGA pairs. However, there was no strong agreement between the mentioned pairs. CONCLUSION: Nutritional status of patients hospitalized in ICU wards in Iran wassomewhat better than other countries that this could be due to the highly observed guidelines of patient's care in Iran. Anyway,it is suggested that a more precise tool of nutritional scoresto be validated for patients hospitalized in ICU·In addition, better medical care needs a well evaluation of nutritional insufficiencies and what is necessary for compensation using complementary regimens.

An application of association rule mining to extract risk pattern for type 2 diabetes using tehran lipid and glucose study database.

BACKGROUND: Type 2 diabetes, common and serious global health concern, had an estimated worldwide prevalence of 366 million in 2011, which is expected to rise to 552 million people, by 2030, unless urgent action is taken. OBJECTIVES: The aim of this study was to identify risk patterns for type 2 diabetes incidence using association rule mining (ARM). PATIENTS AND METHODS: A population of 6647 individuals without diabetes, aged ≥ 20 years at inclusion, was followed for 10-12 years, to analyze risk patterns for diabetes occurrence. Study variables included demographic and anthropometric characteristics, smoking status, medical and drug history and laboratory measures. RESULTS: In the case of women, the results showed that impaired fasting glucose (IFG) and impaired glucose tolerance (IGT), in combination with body mass index (BMI) ≥ 30 kg/m(2), family history of diabetes, wrist circumference > 16.5 cm and waist to height ≥ 0.5 can increase the risk for developing diabetes. For men, a combination of IGT, IFG, length of stay in the city (> 40 years), central obesity, total cholesterol to high density lipoprotein ratio ≥ 5.3, low physical activity, chronic kidney disease and wrist circumference > 18.5 cm were identified as risk patterns for diabetes occurrence. CONCLUSIONS: Our study showed that ARM is a useful approach in determining which combinations of variables or predictors occur together frequently, in people who will develop diabetes. The ARM focuses on joint exposure to different combinations of risk factors, and not the predictors alone.

Applying decision tree for identification of a low risk population for type 2 diabetes. Tehran Lipid and Glucose Study.

AIMS: The aim of this study was to create a prediction model using data mining approach to identify low risk individuals for incidence of type 2 diabetes, using the Tehran Lipid and Glucose Study (TLGS) database. METHODS: For a 6647 population without diabetes, aged ≥20 years, followed for 12 years, a prediction model was developed using classification by the decision tree technique. Seven hundred and twenty-nine (11%) diabetes cases occurred during the follow-up. Predictor variables were selected from demographic characteristics, smoking status, medical and drug history and laboratory measures. RESULTS: We developed the predictive models by decision tree using 60 input variables and one output variable. The overall classification accuracy was 90.5%, with 31.1% sensitivity, 97.9% specificity; and for the subjects without diabetes, precision and f-measure were 92% and 0.95, respectively. The identified variables included fasting plasma glucose, body mass index, triglycerides, mean arterial blood pressure, family history of diabetes, educational level and job status. CONCLUSIONS: In conclusion, decision tree analysis, using routine demographic, clinical, anthropometric and laboratory measurements, created a simple tool to predict individuals at low risk for type 2 diabetes.

Inter-observer and Intra-observer Agreement in Pathological Evaluation of Non-alcoholic Fatty Liver Disease Suspected Liver Biopsies.

BACKGROUND: Histopathologic assessment of liver tissue is an essential step in management and follow-up of non-alcoholic fatty liver disease (NAFLD) while inter- and intra-observer variations limit the accuracy of these assessments. OBJECTIVES: The aim of this study was to assess the inter- and intra-observer reproducibility of histopathologic assessment of liver biopsies based on NAFLD activity score (NAS) scoring system. MATERIALS AND METHODS: The anonymous liver biopsy samples of 100 consecutive NAFLD suspected adults were randomly assigned to four pathologists. Then, the samples were randomly reassigned to the pathologists for the second time in a way that each sample would be evaluated by two different pathologists. Biopsies were revisited by their first evaluator after two months. The results were reported based on NAS scoring system. RESULTS: Inter-observer agreement of the pathology scores based on NAS scoring system was acceptable for steatosis, lobular inflammation, and fibrosis, but not for hepatocyte ballooning. The intra-observer agreement was acceptable in all scales, with lowest intra-class correlation observed for lobular inflammation. CONCLUSIONS: NAS scoring system has good overall inter- and intra-observer agreement, but more attention should be given to defining the hepatocyte ballooning and lobular inflammation, and training the pathologists to improve the accuracy of pathology reports.

A diagnostic model for cirrhosis in patients with non-alcoholic fatty liver disease: an artificial neural network approach.

BACKGROUND: Timely diagnosis of liver cirrhosis is vital for preventing further liver damage and giving the patient the chance of transplantation. Although biopsy of the liver is the gold standard for cirrhosis assessment, it has some risks and limitations and this has led to the development of new noninvasive methods to determine the stage and prognosis of the patients. We aimed to design an artificial neural network (ANN) model to diagnose cirrhosis patients with non-alcoholic fatty liver disease (NAFLD) using routine laboratory data. METHODS: Data were collected from 392 patients with NAFLD by the Middle East Research Center in Tehran. Demographic variables, history of diabetes, INR, complete blood count, albumin, ALT, AST and other routine laboratory tests, examinations and medical history were gathered. Relevant variables were selected by means of feature extraction algorithm (Knime software) and were accredited by the experts. A neural network was developed using the MATLAB software. RESULTS: The best obtained model was developed with two layers, eight neurons and TANSIG and PURLIN functions for layer one and output layer, respectively. The sensitivity and specificity of the model were 86.6% and 92.7%, respectively. CONCLUSION: The results of this study revealed that the neural network modeling may be able to provide a simple, noninvasive and accurate method for diagnosing cirrhosis only based on routine laboratory data.

Designing a Human T-Lymphotropic Virus Type 1 (HTLV-I) Diagnostic Model using the Complete Blood Count.

OBJECTIVE(S): Infection caused by Human T-Lymphotropic Virus Type 1 (HTLV-I) can be observed in some areas of Iran in form of endemic. Most of the cases are asymptomatic, and few cases progress to malignancies and neural diseases. Designing and implementing a model to screen people especially in endemic regions can help timely detection of infected people and improve the prognosis of the disease. MATERIALS AND METHODS: In this study, results of the complete blood count (CBC-diff) for 599 healthy people and the patients with different types of Leukemia and HTLV-I have been examined. Modeling was made using CHAID method. The final model was carried out based on the number of white blood cells (WBC), platelets, and percentages of eosinophils. RESULTS: The accuracy of the final model was 91%. By applying this model to the CBC-diff results of people without symptoms or miscellaneous patients in endemic regions of our country, disease carriers can be identified and referred for supplementary tests. CONCLUSION: With regard to the prevalence of different complications in infected people, these individuals can be identified earlier, leading to the improvement of the prognosis of this disease and the increase of the health status especially in endemic regions.