Displaying publications 61 - 80 of 1046 in total

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  1. Alazzawi S, Shahrizal T, Prepageran N, Pailoor J
    Qatar Med J, 2014;2014(1):57-60.
    PMID: 25320694 DOI: 10.5339/qmj.2014.10
    Isolated sphenoid sinus lesions are an uncommon entity and present with non-specific symptoms. In this case report, the patient presented with a history of headaches for a duration of one month without sinonasal symptoms. A computed tomography scan showed a soft tissue mass occupying the sphenoid sinus. An endoscopic biopsy revealed fungal infection. Endoscopic wide sphenoidotomy with excision of the sphenoid sinus lesion was then performed however, the microbiological examination post-surgery did not show any fungal elements. Instead, Citrobacter species was implicated to be the cause of infection.
    Matched MeSH terms: Tomography, X-Ray Computed
  2. Alazzawi S, Omar R, Rahmat K, Alli K
    Auris Nasus Larynx, 2012 Aug;39(4):393-6.
    PMID: 22055509 DOI: 10.1016/j.anl.2011.10.002
    To ascertain the prevalence of the lateral lamella of the cribriform plate height according to Keros classification in the Malaysian population, and to find if there is any difference between the major ethnic groups in Malaysia (Malay, Chinese, Indian).
    Matched MeSH terms: Tomography, X-Ray Computed
  3. Albadr MAA, Tiun S, Ayob M, Al-Dhief FT, Omar K, Hamzah FA
    PLoS One, 2020;15(12):e0242899.
    PMID: 33320858 DOI: 10.1371/journal.pone.0242899
    The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.
    Matched MeSH terms: Tomography, X-Ray Computed
  4. Albahri OS, Zaidan AA, Albahri AS, Zaidan BB, Abdulkareem KH, Al-Qaysi ZT, et al.
    J Infect Public Health, 2020 Oct;13(10):1381-1396.
    PMID: 32646771 DOI: 10.1016/j.jiph.2020.06.028
    This study presents a systematic review of artificial intelligence (AI) techniques used in the detection and classification of coronavirus disease 2019 (COVID-19) medical images in terms of evaluation and benchmarking. Five reliable databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus were used to obtain relevant studies of the given topic. Several filtering and scanning stages were performed according to the inclusion/exclusion criteria to screen the 36 studies obtained; however, only 11 studies met the criteria. Taxonomy was performed, and the 11 studies were classified on the basis of two categories, namely, review and research studies. Then, a deep analysis and critical review were performed to highlight the challenges and critical gaps outlined in the academic literature of the given subject. Results showed that no relevant study evaluated and benchmarked AI techniques utilised in classification tasks (i.e. binary, multi-class, multi-labelled and hierarchical classifications) of COVID-19 medical images. In case evaluation and benchmarking will be conducted, three future challenges will be encountered, namely, multiple evaluation criteria within each classification task, trade-off amongst criteria and importance of these criteria. According to the discussed future challenges, the process of evaluation and benchmarking AI techniques used in the classification of COVID-19 medical images considered multi-complex attribute problems. Thus, adopting multi-criteria decision analysis (MCDA) is an essential and effective approach to tackle the problem complexity. Moreover, this study proposes a detailed methodology for the evaluation and benchmarking of AI techniques used in all classification tasks of COVID-19 medical images as future directions; such methodology is presented on the basis of three sequential phases. Firstly, the identification procedure for the construction of four decision matrices, namely, binary, multi-class, multi-labelled and hierarchical, is presented on the basis of the intersection of evaluation criteria of each classification task and AI classification techniques. Secondly, the development of the MCDA approach for benchmarking AI classification techniques is provided on the basis of the integrated analytic hierarchy process and VlseKriterijumska Optimizacija I Kompromisno Resenje methods. Lastly, objective and subjective validation procedures are described to validate the proposed benchmarking solutions.
    Matched MeSH terms: Tomography, X-Ray Computed/classification*
  5. Albayrak E, Sonmezgoz F, Ozmen Z, Aktas F, Altunkas A
    Malays J Med Sci, 2017 Oct;24(5):112-118.
    PMID: 29386979 DOI: 10.21315/mjms2017.24.5.13
    A 26-year-old female patient with Type 1 Gaucher's disease (GD) was admitted to our clinic with complaints of stomachache and signs of anemia. The patient underwent ultrasonography (US), computerised tomography (CT), and magnetic resonance imaging (MRI) scan. Imaging studies revealed massive hepatosplenomegaly, choledocolithiasis, and six nodules in the spleen with a mean size of 14 mm. The nodules appeared hyperechoic, hypoechoic, and of mixed echogenicity on the US and hypodense on the CT. While the nodules were observed to be iso-hypointense in T1-weighted (T1WI) images, they appeared to be hyperintense in the T2-weighted (T2WI) images. There were no diffusion restrictions in these nodules that appeared on the diffusion-weighted magnetic resonance imaging (DWI). A nodule located at the lower pole was observed to be hypointense in the T2WI images. The nodule located at the lower pole, which appeared hypointense in T2WI series, had restricted diffusion upon DWI. In this study, we aimed to present the properties of splenic GD nodules using US, CT, and conventional MRI, together with DWI. This case report is the first to apply US, CT, and conventional MRI, together with DWI, to the splenic nodules associated with Gaucher's disease.
    Matched MeSH terms: Tomography, X-Ray Computed
  6. Alhamad T
    Am J Respir Crit Care Med, 2011 Aug 15;184(4):484.
    PMID: 21844517 DOI: 10.1164/rccm.201012-2018IM
    Matched MeSH terms: Tomography, X-Ray Computed
  7. Alhorani Q, Alkhybari E, Rawashdeh M, Sabarudin A, Latiff RA, Al-Ibraheem A, et al.
    Nucl Med Commun, 2023 Nov 01;44(11):937-943.
    PMID: 37615527 DOI: 10.1097/MNM.0000000000001748
    PET-computed tomography (PET/CT) is a hybrid imaging technique that combines anatomical and functional information; to investigate primary cancers, stage tumours, and track treatment response in paediatric oncology patients. However, there is debate in the literature about whether PET/CT could increase the risk of cancer in children, as the machine is utilizing two types of radiation, and paediatric patients have faster cell division and longer life expectancy. Therefore, it is essential to minimize radiation exposure by justifying and optimizing PET/CT examinations and ensure an acceptable image quality. Establishing diagnostic reference levels (DRLs) is a crucial quantitative indicator and effective tool to optimize paediatric imaging procedures. This review aimed to distinguish and acknowledge variations among published DRLs for paediatric patients in PET/CT procedures. A search of relevant articles was conducted using databases, that is, Embase, Scopus, Web of Science, and Medline, using the keywords: PET-computed tomography, computed tomography, PET, radiopharmaceutical, DRL, and their synonyms. Only English and full-text articles were included, with no limitations on the publication year. After the screening, four articles were selected, and the review reveals different DRL approaches for paediatric patients undergoing PET/CT, with primary variations observed in patient selection criteria, reporting of radiation dose values, and PET/CT equipment. The study suggests that future DRL methods for paediatric patients should prioritize data collection in accordance with international guidelines to better understand PET/CT dose discrepancies while also striving to optimize radiation doses without compromising the quality of PET/CT images.
    Matched MeSH terms: Tomography, X-Ray Computed
  8. Ali AA, Gurung R, Hayati F, Zakaria AD, Mohamad I, Ching FF
    Wilderness Environ Med, 2021 Dec;32(4):517-521.
    PMID: 34479771 DOI: 10.1016/j.wem.2021.07.006
    Encounters between marine animals and humans can result in critical injury and fatal complications. We highlight a 20-y-old male who sustained a penetrating injury to the neck as a result of impalement by needlefish (Tylosurus sp) while snorkeling. He sustained a penetrating injury in the posterior triangle of the neck. On presentation, he was stabilized and received empirical antibiotics, analgesia, and antitetanus toxoid injection before being transferred to a tertiary center. On presentation to the tertiary hospital, the patient was hemodynamically stable with no clinical evidence of injury to surrounding neck structures, and this was confirmed using computed tomography. The patient underwent local wound exploration and retrieval of the needlefish beak under general anesthesia. The wound was left open to heal by secondary intention. The patient was discharged with oral antibiotics and went on to make a complete recovery.
    Matched MeSH terms: Tomography, X-Ray Computed
  9. Alias A, Ibrahim A, Abu Bakar SN, Swarhib Shafie M, Das S, Abdullah N, et al.
    Clin Ter, 2018 11 6;169(5):e217-e223.
    PMID: 30393808 DOI: 10.7417/CT.2018.2082
    INTRODUCTION: The first step in the forensic identification is sex determination followed by age and stature estimation, as both are sex-dependent. The mandible is the largest, strongest and most durable bone in the face. Mandible is important for sex confirmation in absence of a complete pelvis and skull.

    AIM: The aim of the present study was to determine sex of human mandible from morphology, morphometric measurements as well as discriminant function analysis from the CT scan.

    MATERIALS AND METHODS: The present retrospective study comprised 79 subjects (48 males, 31 females), with age group between 18 and 74 years, and were obtained from the post mortem computed tomography data in the Hospital Kuala Lumpur. The parameters were divided into three morphologic and nine morphometric parameters, which were measured by using Osirix MD Software 3D Volume Rendering.

    RESULTS: The Chi-square test showed that men were significantly association with square-shaped chin (92%), prominent muscle marking (85%) and everted gonial glare, whereas women had pointed chin (84%), less prominent muscle marking (90%) and inverted gonial glare (80%). All parameter measurements showed significantly greater values in males than in females by independent t-test (p< 0.01). By discriminant analysis, the classification accuracy was 78.5%, the sensitivity was 79.2% and the specificity was 77.4%. The discriminant function equation was formulated based on bigonial breath and condylar height, which were the best predictors.

    CONCLUSION: In conclusion, the mandible could be distinguished according to the sex. The results of the study can be used for identification of damaged and/or unknown mandible in the Malaysian population.

    Matched MeSH terms: Tomography, X-Ray Computed
  10. Alias H, Doris Lau SC, Loh CK, Ishak MI, Mohammed F, Jamal R, et al.
    J Pediatr Hematol Oncol, 2017 11;39(8):e463-e465.
    PMID: 28859035 DOI: 10.1097/MPH.0000000000000960
    Giant cell tumor (GCT) is one of the most common tumors of bone and is the most common precursor of aneurysmal bone cysts (ABC). The clinical behavior of concurrent GCT and ABC can be very aggressive in children. GCT of the ribs, with or without ABC, is rarely seen in children. We report a case of an 8-year-old girl with GCT and associated ABC of the ribs who presented with sudden onset of chest pain and breathlessness due to a hemothorax. The patient was successfully treated by surgical resections and arterial embolization. She has remained well for 4 years after the initial surgery.
    Matched MeSH terms: Tomography, X-Ray Computed
  11. Alif Adlan MT, Wan Mohd Rasis WA, Mohd Ramadhan MD
    Med J Malaysia, 2016 04;71(2):72-3.
    PMID: 27326946 MyJurnal
    Staphylococcus Aureus is a Gram-positive cocci bacteria which had been found to be the causative organism in over 88% of patients with primary iliopsoas abscess. We report the case of a 53-year-old diabetic woman with end-stage renal failure diagnosed with left iliopsoas abscess with a catheter-related infection. Computed tomogram (CT) of abdomen and pelvis revealed hypodense lesions of left psoas, iliacus and quadratus lumborum suggestive of psoas abscesses. In addition, osteomyelitis changes at left sacroiliac and hip joint were seen. At surgery, she was found to have abscess at the posterior psoas muscle where she underwent open surgery drainage and percutaneous drain was inserted. A high index of suspicion of iliopsoas abscess should be maintained among haemodialysis patients presenting with intradialytic pelvic and hip pain and treated with optimal antibiotics therapy with appropriate surgical intervention.
    Matched MeSH terms: Tomography, X-Ray Computed
  12. Alirr OI, Rahni AAA
    J Digit Imaging, 2020 04;33(2):304-323.
    PMID: 31428898 DOI: 10.1007/s10278-019-00262-8
    Preoperative planning for liver surgical treatments is an essential planning tool that aids in reducing the risks of surgical resection. Based on the computed tomography (CT) images, the resection can be planned before the actual tumour resection surgery. The computer-aided system provides an overview of the spatial relationships of the liver organ and its internal structures, tumours, and vasculature. It also allows for an accurate calculation of the remaining liver volume after resection. The aim of this paper was to review the main stages of the computer-aided system that helps to evaluate the risk of resection during liver cancer surgical treatments. The computer-aided system assists with surgical planning by enabling physicians to get volumetric measurements and visualise the liver, tumours, and surrounding vasculature. In this paper, it is concluded that for accurate planning of tumour resections, the liver organ and its internal structures should be segmented to understand the clear spatial relationship between them, thus allowing for a safer resection. This paper presents the main proposed segmentation techniques for each stage in the computer-aided system, namely the liver organ, tumours, and vessels. From the reviewed methods, it has been found that instead of relying on a single specific technique, a combination of a group of techniques would give more accurate segmentation results. The extracted masks from the segmentation algorithms are fused together to give the surgeons the 3D visualisation tool to study the spatial relationships of the liver and to calculate the required resection planning parameters.
    Matched MeSH terms: Tomography, X-Ray Computed
  13. Alirr OI, Rahni AAA, Golkar E
    Int J Comput Assist Radiol Surg, 2018 Aug;13(8):1169-1176.
    PMID: 29860549 DOI: 10.1007/s11548-018-1801-z
    PURPOSE: Segmentation of liver tumours is an important part of the 3D visualisation of the liver anatomy for surgical planning. The spatial relationship between tumours and other structures inside the liver forms the basis of preoperative surgical risk assessment. However, the automatic segmentation of liver tumours from abdominal CT scans is riddled with challenges. Tumours located at the border of the liver impose a big challenge as the surrounding tissues could have similar intensities.

    METHODS: In this work, we introduce a fully automated liver tumour segmentation approach in contrast-enhanced CT datasets. The method is a multi-stage technique which starts with contrast enhancement of the tumours using anisotropic filtering, followed by adaptive thresholding to extract the initial mask of the tumours from an identified liver region of interest. Localised level set-based active contours are used to extend the mask to the tumour boundaries.

    RESULTS: The proposed method is validated on the IRCAD database with pathologies that offer highly variable and complex liver tumours. The results are compared quantitatively to the ground truth, which is delineated by experts. We achieved an average dice similarity coefficient of 75% over all patients with liver tumours in the database with overall absolute relative volume difference of 11%. This is comparable to other recent works, which include semiautomated methods, although they were validated on different datasets.

    CONCLUSIONS: The proposed approach aims to segment tumours inside the liver envelope automatically with a level of accuracy adequate for its use as a tool for surgical planning using abdominal CT images. The approach will be validated on larger datasets in the future.

    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  14. Alizadehsani R, Abdar M, Roshanzamir M, Khosravi A, Kebria PM, Khozeimeh F, et al.
    Comput Biol Med, 2019 08;111:103346.
    PMID: 31288140 DOI: 10.1016/j.compbiomed.2019.103346
    Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and often leads to a heart attack. It annually causes millions of deaths and billions of dollars in financial losses worldwide. Angiography, which is invasive and risky, is the standard procedure for diagnosing CAD. Alternatively, machine learning (ML) techniques have been widely used in the literature as fast, affordable, and noninvasive approaches for CAD detection. The results that have been published on ML-based CAD diagnosis differ substantially in terms of the analyzed datasets, sample sizes, features, location of data collection, performance metrics, and applied ML techniques. Due to these fundamental differences, achievements in the literature cannot be generalized. This paper conducts a comprehensive and multifaceted review of all relevant studies that were published between 1992 and 2019 for ML-based CAD diagnosis. The impacts of various factors, such as dataset characteristics (geographical location, sample size, features, and the stenosis of each coronary artery) and applied ML techniques (feature selection, performance metrics, and method) are investigated in detail. Finally, the important challenges and shortcomings of ML-based CAD diagnosis are discussed.
    Matched MeSH terms: Tomography, X-Ray Computed
  15. Aljarrah K, Packirisamy V, Al Anazi N, Nayak SB
    Morphologie, 2022 Dec;106(355):260-270.
    PMID: 34391659 DOI: 10.1016/j.morpho.2021.07.006
    OBJECTIVE OF THE STUDY: The objective of this study was to evaluate FM and occipital condyles measurements morphometrically for sex determination by using discriminant function analysis and to note visually the variation in the shape of the foramen magnum in a Saudi Arabian population by using CT scan images.

    MATERIAL AND METHODS: This study included 472 CT scans (236 males and 236 females; age range, 18-72 years). The foramen magnum shapes were classified into 8 types: oval, egg, round, hexagonal, pentagonal, tetragonal, irregular (A) and irregular (B). The intraobserver and interobserver test was done to calculate the reliability of the measurement. Eight dimensions of the FM and occipital condyle were evaluated to determine the sexual dimorphism using an independent t-test. Sex determination was estimated using discriminate function analysis.

    RESULTS: The commonest shape of FM was hexagonal and the tetragonal shape was the least common type. Coefficient of reliability (R) was high, ranging between 0.89 and 0.99, which indicates the measurements are reliable and sufficiently precise. All the eight measurements, the FM length and width, FM index, FM area, the width and length of right and left occipital condyles were significantly greater in males than the female. Univariate discriminant function showed an accuracy rate varying from 61% to 66.6% based on FM or occipital condyles measurements. The multivariate analysis of FM and occipital condyle measurements increased the overall accuracy rate of sex determination to 71.6%.

    CONCLUSION: The univariate analysis of FM and occipital condyle measurements indicates, that the FM area (66.1%), FML (62.5%), FMW (62.5%) and ROCL (62.1%) could be reliable individual variables in sex determination. The multivariate analysis including all the eight variables of FM and occipital condyle increased the accuracy rate of sex determination to 71.6% in determining the sex as male (73.3%) or female (69.9%). The shape of the FM is not useful in sex estimation. The results obtained showed a low degree of sexual dimorphism in the basicranium, the use of this method in forensic anthropology could be helpful for assessment on highly fragmented skull bases.

    Matched MeSH terms: Tomography, X-Ray Computed
  16. Alkhorayef M, Sulieman A, Alzahrani K, Abuzaid M, Alomair OI, Almuwannis M, et al.
    Appl Radiat Isot, 2021 Feb;168:109520.
    PMID: 33307438 DOI: 10.1016/j.apradiso.2020.109520
    The various technological advancements in computed tomography (CT) have resulted in remarkable growth in the use of CT imaging in clinical practice, not the least of which has been its establishment as the most valuable imaging examination for the assessment of cardiovascular system disorders. The objective of this study was to assess the effective radiation dose and radiation risk for patients during cardiac CT procedures, based on studies from four different hospitals equipped with 128 slice CT equipment. A total of eighty-three patients were investigated in this study with different clinical indications. Effective doses were also calculated using software based on Monte Carlo simulation. The mean patient age (years), weight (kg), and body mass index (BMI (kg/m2)) were 49 ± 11, 82 ± 12, and 31 ± 6, respectively. The results of the study revealed that the tube voltage (kVp) and tube current-exposure time product (mAs) ranged between 100 to 140 and 50 to 840 respectively. The overall average patient dose values for the volume CT dose index [(CTDIvol), in mGy)] and dose length product (DLP) (in mGy·cm) were 34.8 ± 15 (3.7-117.0) and 383.8 ± 354 (46.0-3277.0) respectively. The average effective dose (mSv) was 15.2 ± 8 (1.2-61.8). The radiation dose values showed wide variation between different hospitals and even within the same hospital. The results indicate the need to optimize radiation dose and to establish diagnostic reference levels (DRLs) for patients undergoing coronary computed tomography angiography (CCTA), also to harmonize the imaging protocols to ensure reduced radiation risk.
    Matched MeSH terms: Tomography, X-Ray Computed
  17. Almothafar, B., Wong, L., Noorafidah, M.D.
    JUMMEC, 2011;14(1):26-30.
    MyJurnal
    Primary parotid lymphoma is uncommon and rarely suspected. In most cases, the disease would have disseminated at the time of diagnosis. We describe a case of primary non-Hodgkin’s lymphoma of the parotid gland which progressed to the central nervous system. Clinical history is of limited value in identifying this condition. Diagnostic imaging studies (CT or MRI) may indicate whether or not the mass is salivary in origin but do not help to confirm the diagnosis. In this case, it was deemed that FNA alone is incapable of determining the precise histological subtype for lymphoma, whilst a tru-cut biopsy demonstrated a more sensitive method of determining the diagnosis. The lessons learned from this case would prove useful for other health care providers to make an early diagnosis and hopefully manage more effectively if similar conditions appear in their practice. Performing the appropriate measures can help to not only improve the prognosis but may even avert the prospect of unnecessary surgery.
    Matched MeSH terms: Tomography, X-Ray Computed
  18. Aloysius, A., Seed, H.F., Thong, K.S.
    MyJurnal
    Schizencephaly is an uncommon congenital malformation of the central
    nervous system which affects the development of the cerebral cortex. It is
    defined as the gray matter lined filled cleft with cerebrospinal fluid that
    extends from the pial surface to the ventricle. There exists a paucity in
    scientific literature regarding the association between schizencephaly and
    psychosis. We report a case of a 36-year-old male, presented with worsening
    crisis of aggression, disorganized behavior and auditory hallucinations over
    20 years. CT brain scan revealed unilateral left open lip schizencephaly, a
    finding during his recent admission to the psychiatry ward.
    Matched MeSH terms: Tomography, X-Ray Computed
  19. Alsulaimy M, Punchai S, Ali FA, Kroh M, Schauer PR, Brethauer SA, et al.
    Obes Surg, 2017 Aug;27(8):1924-1928.
    PMID: 28229315 DOI: 10.1007/s11695-017-2590-0
    PURPOSE: Chronic abdominal pain after bariatric surgery is associated with diagnostic and therapeutic challenges. The aim of this study was to evaluate the yield of laparoscopy as a diagnostic and therapeutic tool in post-bariatric surgery patients with chronic abdominal pain who had negative imaging and endoscopic studies.

    METHODS: A retrospective analysis was performed on post-bariatric surgery patients who underwent laparoscopy for diagnosis and treatment of chronic abdominal pain at a single academic center. Only patients with both negative preoperative CT scan and upper endoscopy were included.

    RESULTS: Total of 35 post-bariatric surgery patients met the inclusion criteria, and all had history of Roux-en-Y gastric bypass. Twenty out of 35 patients (57%) had positive findings on diagnostic laparoscopy including presence of adhesions (n = 12), chronic cholecystitis (n = 4), mesenteric defect (n = 2), internal hernia (n = 1), and necrotic omentum (n = 1). Two patients developed post-operative complications including a pelvic abscess and an abdominal wall abscess. Overall, 15 patients (43%) had symptomatic improvement after laparoscopy; 14 of these patients had positive laparoscopic findings requiring intervention (70% of the patients with positive laparoscopy). Conversely, 20 (57%) patients required long-term medical treatment for management of chronic abdominal pain.

    CONCLUSION: Diagnostic laparoscopy, which is a safe procedure, can detect pathological findings in more than half of post-bariatric surgery patients with chronic abdominal pain of unknown etiology. About 40% of patients who undergo diagnostic laparoscopy and 70% of patients with positive findings on laparoscopy experience significant symptom improvement. Patients should be informed that diagnostic laparoscopy is associated with no symptom improvement in about half of cases.

    Matched MeSH terms: Tomography, X-Ray Computed
  20. Alzu'bi D, Abdullah M, Hmeidi I, AlAzab R, Gharaibeh M, El-Heis M, et al.
    J Healthc Eng, 2022;2022:3861161.
    PMID: 37323471 DOI: 10.1155/2022/3861161
    Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist's workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models; three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results; they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist's workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease's track and preserve the patient's life.
    Matched MeSH terms: Tomography, X-Ray Computed/methods
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