Tehran University Medical Journal

Background: Determining the rank of important parameters in ranking health care and providing health services to patients in medical centers.
Methods: The research method is descriptive-analytical and applied in terms of classification. The data is from the database of the Faculty of Health and Medicine of the University of Tehran, which was collected as an archive from April 1998 to May 1999. The statistical population were all experts, specialists and experts of the Faculty of Health 29 of whom completed the questionnaire. The weight of the criteria was evaluated using the AHP method and in the next stage, the medical centers were ranked using the DS-VIKOR method. The analysis method in this study consists of the following steps steps:
• Implementing a simple VIKOR method.
• Implementing the Dempster-Shafer and Vicker method.
• Implementing the AHP method
• Implementation of the Topsis method.
• Comparing the proposed methods to review and compare which ones work best.

The model can investigate the effectiveness of giving caring and providing health services to patients in medical centers.
Conclusion: By combining the two methods of Dempster-Shafer and Vicker, the confidence in the whole uncertainty is improved and the results are more reliable. This approach can help reduce the uncertainty caused by people's cognition to increase the level of decision-making, allowing us to overcome the problem of choosing the right level of uncertainty and to deal with uncertainty in a practical and justified way.

Background: Obesity and overweight are one of the components of metabolic syndrome and the cause of cardiovascular disease and sudden cardiac death. Obesity is associated with a wide range of electrocardiogram (ECG) abnormalities.
Methods: This case-control study was performed on 50 children and adolescents aged 9 to 18 years in Birjand from May to October 2020. In the control group, 25 people with normal weight and in the case group, 25 people with obesity or overweight were included in the study. Individuals with a body mass index of 85-95 percent were defined as overweight, ones with a body mass index above the 95th percentile were defined as obese, and individuals with a body mass index below the 85th percentile were defined as normal. After clinical examination, height, weight and electrocardiogram indices were measured and compared by using statistical tests by SPSS (Version 19) software.

Results: There were 15 boys in the control group and 17 boys in the case group. The mean age of the control and case groups was 11.28±2.13 and 10.96±1.97 years, respectively. The mean distance between the peak to the end of the T wave in the case group was 323.72±120.15 and in the control group was 79.20±13.06. The mean difference between the shortest and longest distance of TP-e in case group was 48±23.04 and in control group was 18.44±5.58, respectively. There was a statistically significant difference between the two indices (P<0.001). But in other variables, no statistically significant difference was observed between the two groups.

Background: Double lumen tube (DLT) is used in lung surgeries. Classically, the patient should undergo fiberoptic bronchoscopy (FOB) to confirm the location of the DLT and its proper function. However, the sensitivity of ultrasound and clinical methods in diagnosing the correct position of DLT has not yet been definitively determined. This study was designed to assess the accuracy of point-of-care ultrasound and auscultation versus Fiberoptic Bronchoscope in determining the position of the Double-Lumen Tube.
Methods: This cross-sectional study of diagnostic value measurement type was conducted on patients who were candidates for double lumen implantation. After induction of anesthesia, DLT with the appropriate size was implanted, and then the position of DLT was evaluated. In the first step, the lungs were examined by auscultation, then the ultrasound was performed, and two signs of lung pulse sign and lung sliding sign were examined as signs of normal lung and ventilated lung. FOB was performed by an anesthesiologist. At the end, by opening the chest after surgery, the surgeon's opinion about the quality of lung collapse was recorded.

Results: In our study, the correct placement of the tube was correct in 37 cases and wrong in 3 cases, which were checked and corrected by FOB. Vital signs of the patients were stable before and during the operation. There were no problems with anesthesia during the surgery. Diagnostic sensitivity of lung auscultation clinical examination was 64.9% and chest ultrasound was 91.9%. The sensitivity of ultrasound compared to auscultation was not significant (P=0.242), but there was a clinically significant difference in the positive predictive value of the two, so that the positive predictive value of lung auscultation was 88.9% and lung ultrasound was 91.9%. In terms of surgeon satisfaction level, 22 cases (59.5%) had excellent satisfaction and 15 cases (40.5%) had moderate satisfaction. The sensitivity of ultrasound was not significant in comparison with the surgeon's satisfaction. Conclusion: Ultrasound can be a good substitute for FOB. Although ultrasound cannot have all the functions of FOB, but having advantages such as lower cost, speed of operation, and non-invasiveness, makes it more practical than FOB.

Methods: This study used machine learning systems and similarity metrics to determine the behavior pattern of COVID-19 in different seasons of the year. The location of research was the Mousa ibn Ja'far Hospital in Mashhad, and the time was from May 2020 to August 2021. The symptoms of affected patients were compared with the compiled dataset, and the similarity of patients was prepared in a similarity matrix, and the Jaccard correlation coefficient was calculated on the data. Finally, the analysis of strains from the beginning of emergence to the latest strain was examined. The performance indicators of the algorithm in the Jaccard similarity method showed a recall metric with a value of 0.94, a precision metric with a value of 1, an F1 score with a value of 0.86, and remove accuracy metric with a value of 0.76. The most important factors in the investigation include white blood cells, platelets, RT-PCR, CT SCAN, shortness of breath, fever, SPO2, and respiratory rate. Results: The transmission of the COVID-19 virus depends on several factors, including human interaction. The evidence of the collected data shows that people with COVID-19 have low lymphocyte count and it is very consistent with the results of recent studies. Due to the lack of a dataset, a comparative study was conducted and a dataset was collected. Conclusion: This study, leveraging machine learning algorithms, identified a clear seasonal correlation in the spread of COVID-19. Considering geographical and seasonal variations among patients, distinct symptoms were observed in each season corresponding to the prevalent strain during that period.

With the rapid expansion of artificial intelligence across clinical disciplines, a variety of artificial neural networks (ANNs) have become indispensable tools for endowing computer systems with advanced analytical power. Dentistry, as an information‑rich branch of medicine, routinely generates and must interpret large, complex datasets from imaging and diagnostic records. Consequently, researchers have increasingly directed their attention toward intelligent, automated techniques for analyzing dental data. This study therefore surveys and synthesizes the methods that have been applied to the intelligent and automated analysis of such data, highlighting the prevailing trends in current literature.The majority of the examined investigations relied on panoramic radiographic images of the teeth orthopantomograms (OPG) as their primary source material. Three overarching technical objectives repeatedly emerged: first, tooth diagnosis, meaning the reliable separation and identification of each individual tooth from its neighbors; second, sample segmentation, that is, the piece‑by‑piece analysis of visual information within the image; and third, semantic segmentation, namely, the contextual interpretation of information extracted from the radiograph. Depending upon which of these objectives was pursued, researchers selected different neural‑network architectures and configurations. Across the reviewed corpus, input images were typically subjected to preprocessing steps such as normalization, noise reduction, and contrast enhancement before being supplied to a neural network for training, thereby preparing the data for subsequent machine interpretation. In several instances, the raw output produced by the neural network underwent additional post‑processing, a stage designed to refine the preliminary results and enhance overall accuracy. The comparative analysis presented here concentrates on how effectively the various neural‑network models fulfilled the three technical objectives described above. The surveyed articles reveal two dominant analytical approaches. In the intelligent problem‑solving paradigm, convolutional neural networks (CNNs) overwhelmingly predominate. Conversely, in the automated paradigm, investigators favor classical, non‑learning algorithmic techniques. Work employing ANNs consistently emphasizes image comprehension, segmentation, feature extraction, feature classification, network modeling, and careful variable tuning to promote effective learning that aligns with each study’s stated objectives.

Background: Congenital tuberculosis is a rare but serious disease in neonates and infants that often presents with nonspecific respiratory symptoms. Pulmonary involvement in tuberculosis can have similar manifestations to bacterial pneumonia with common microorganisms. In case of failure to respond to treatment in pneumonia,  tuberculosis infection should be considered. The aim of this study was to present a two-month-old infant suspected of bacterial pneumonia, who was ultimately diagnosed with tuberculosis.
Case Presentation: This case report describes a two-month-old infant diagnosed with tuberculosis who presented to the emergency department with severe respiratory distress.  Despite repeated hospitalizations and initial antibiotic therapy, the patient's symptoms did not improve and he was eventually referred to the Children's Medical Center. Chest radiography showed diffuse reticular opacities, alveolar opacities in the lower lobe of the right lung, and parahilar opacities in the left lung. Initial laboratory tests included elevated CRP and ESR levels, elevated white blood cell count, thrombocytosis, and abnormal arterial blood gases. Despite three negative gastric aspirate samples for tuberculosis, bronchoscopy was performed and a Bronchoalveolar Lavage (BAL) sample was sent for Polymerase Chain Reaction (PCR) testing for Mycobacterium tuberculosis, which was positive, confirming the diagnosis of tuberculosis. Careful evaluation of the parents revealed that although they had no respiratory symptoms, the mother had imaging evidence of tuberculosis, and her AFB test was positive. The patient showed significant clinical improvement after starting anti-tuberculosis therapy. A six-month follow-up confirmed complete recovery.
Conclusion: In infants with recurrent pneumonia and failure to respond to initial treatments, tuberculosis should be considered as a possible diagnosis.