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Fulltext The communication of artificial intelligence and deep learning in computer tomography image recognition of epidemic pulmonary infectious diseases

Wang W, Zhao X, Jia Y, Xu J

PLoS One, 2024;19(2):e0297578.
PMID: 38319912 DOI: 10.1371/journal.pone.0297578

The objectives are to improve the diagnostic efficiency and accuracy of epidemic pulmonary infectious diseases and to study the application of artificial intelligence (AI) in pulmonary infectious disease diagnosis and public health management. The computer tomography (CT) images of 200 patients with pulmonary infectious disease are collected and input into the AI-assisted diagnosis software based on the deep learning (DL) model, "UAI, pulmonary infectious disease intelligent auxiliary analysis system", for lesion detection. By analyzing the principles of convolutional neural networks (CNN) in deep learning (DL), the study selects the AlexNet model for the recognition and classification of pulmonary infection CT images. The software automatically detects the pneumonia lesions, marks them in batches, and calculates the lesion volume. The result shows that the CT manifestations of the patients are mainly involved in multiple lobes and density, the most common shadow is the ground-glass opacity. The detection rate of the manual method is 95.30%, the misdetection rate is 0.20% and missed diagnosis rate is 4.50%; the detection rate of the DL-based AI-assisted lesion method is 99.76%, the misdetection rate is 0.08%, and the missed diagnosis rate is 0.08%. Therefore, the proposed model can effectively identify pulmonary infectious disease lesions and provide relevant data information to objectively diagnose pulmonary infectious disease and manage public health.

Matched MeSH terms: Tomography, X-Ray Computed/methods
The potential role of artificial intelligence-assisted chest X-ray imaging in detecting early-stage lung cancer in the community-a proposed algorithm for lung cancer screening in Malaysia

Sachithanandan A, Lockman H, Azman RR, Tho LM, Ban EZ, Ramon V

Med J Malaysia, 2024 Jan;79(1):9-14.
PMID: 38287751

INTRODUCTION: The poor prognosis of lung cancer has been largely attributed to the fact that most patients present with advanced stage disease. Although low dose computed tomography (LDCT) is presently considered the optimal imaging modality for lung cancer screening, its use has been hampered by cost and accessibility. One possible approach to facilitate lung cancer screening is to implement a risk-stratification step with chest radiography, given its ease of access and affordability. Furthermore, implementation of artificial-intelligence (AI) in chest radiography is expected to improve the detection of indeterminate pulmonary nodules, which may represent early lung cancer.
MATERIALS AND METHODS: This consensus statement was formulated by a panel of five experts of primary care and specialist doctors. A lung cancer screening algorithm was proposed for implementation locally.
RESULTS: In an earlier pilot project collaboration, AI-assisted chest radiography had been incorporated into lung cancer screening in the community. Preliminary experience in the pilot project suggests that the system is easy to use, affordable and scalable. Drawing from experience with the pilot project, a standardised lung cancer screening algorithm using AI in Malaysia was proposed. Requirements for such a screening programme, expected outcomes and limitations of AI-assisted chest radiography were also discussed.
CONCLUSION: The combined strategy of AI-assisted chest radiography and complementary LDCT imaging has great potential in detecting early-stage lung cancer in a timely manner, and irrespective of risk status. The proposed screening algorithm provides a guide for clinicians in Malaysia to participate in screening efforts.

Matched MeSH terms: Tomography, X-Ray Computed/methods
Fulltext Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images

Alsaih K, Lemaitre G, Rastgoo M, Massich J, Sidibé D, Meriaudeau F

Biomed Eng Online, 2017 Jun 07;16(1):68.
PMID: 28592309 DOI: 10.1186/s12938-017-0352-9

BACKGROUND: Spectral domain optical coherence tomography (OCT) (SD-OCT) is most widely imaging equipment used in ophthalmology to detect diabetic macular edema (DME). Indeed, it offers an accurate visualization of the morphology of the retina as well as the retina layers.
METHODS: The dataset used in this study has been acquired by the Singapore Eye Research Institute (SERI), using CIRRUS TM (Carl Zeiss Meditec, Inc., Dublin, CA, USA) SD-OCT device. The dataset consists of 32 OCT volumes (16 DME and 16 normal cases). Each volume contains 128 B-scans with resolution of 1024 px × 512 px, resulting in more than 3800 images being processed. All SD-OCT volumes are read and assessed by trained graders and identified as normal or DME cases based on evaluation of retinal thickening, hard exudates, intraretinal cystoid space formation, and subretinal fluid. Within the DME sub-set, a large number of lesions has been selected to create a rather complete and diverse DME dataset. This paper presents an automatic classification framework for SD-OCT volumes in order to identify DME versus normal volumes. In this regard, a generic pipeline including pre-processing, feature detection, feature representation, and classification was investigated. More precisely, extraction of histogram of oriented gradients and local binary pattern (LBP) features within a multiresolution approach is used as well as principal component analysis (PCA) and bag of words (BoW) representations.
RESULTS AND CONCLUSION: Besides comparing individual and combined features, different representation approaches and different classifiers are evaluated. The best results are obtained for LBP[Formula: see text] vectors while represented and classified using PCA and a linear-support vector machine (SVM), leading to a sensitivity(SE) and specificity (SP) of 87.5 and 87.5%, respectively.

Matched MeSH terms: Tomography, Optical Coherence*
Fulltext Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans

Kundu R, Basak H, Singh PK, Ahmadian A, Ferrara M, Sarkar R

Sci Rep, 2021 Jul 08;11(1):14133.
PMID: 34238992 DOI: 10.1038/s41598-021-93658-y

COVID-19 has crippled the world's healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are being tested and supplied around the world, it will take a long time to reach every human being, more so with new variants of the virus emerging, enforcing a lockdown-like situation on parts of the world. Thus, there is a dire need for early and accurate detection of COVID-19 to prevent the spread of the disease, even more. The current gold-standard RT-PCR test is only 71% sensitive and is a laborious test to perform, leading to the incapability of conducting the population-wide screening. To this end, in this paper, we propose an automated COVID-19 detection system that uses CT-scan images of the lungs for classifying the same into COVID and Non-COVID cases. The proposed method applies an ensemble strategy that generates fuzzy ranks of the base classification models using the Gompertz function and fuses the decision scores of the base models adaptively to make the final predictions on the test cases. Three transfer learning-based convolutional neural network models are used, namely VGG-11, Wide ResNet-50-2, and Inception v3, to generate the decision scores to be fused by the proposed ensemble model. The framework has been evaluated on two publicly available chest CT scan datasets achieving state-of-the-art performance, justifying the reliability of the model. The relevant source codes related to the present work is available in: GitHub.

Matched MeSH terms: Tomography, X-Ray Computed/methods*
Fulltext GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest

Saha P, Mukherjee D, Singh PK, Ahmadian A, Ferrara M, Sarkar R

Sci Rep, 2021 04 15;11(1):8304.
PMID: 33859222 DOI: 10.1038/s41598-021-87523-1

COVID-19, a viral infection originated from Wuhan, China has spread across the world and it has currently affected over 115 million people. Although vaccination process has already started, reaching sufficient availability will take time. Considering the impact of this widespread disease, many research attempts have been made by the computer scientists to screen the COVID-19 from Chest X-Rays (CXRs) or Computed Tomography (CT) scans. To this end, we have proposed GraphCovidNet, a Graph Isomorphic Network (GIN) based model which is used to detect COVID-19 from CT-scans and CXRs of the affected patients. Our proposed model only accepts input data in the form of graph as we follow a GIN based architecture. Initially, pre-processing is performed to convert an image data into an undirected graph to consider only the edges instead of the whole image. Our proposed GraphCovidNet model is evaluated on four standard datasets: SARS-COV-2 Ct-Scan dataset, COVID-CT dataset, combination of covid-chestxray-dataset, Chest X-Ray Images (Pneumonia) dataset and CMSC-678-ML-Project dataset. The model shows an impressive accuracy of 99% for all the datasets and its prediction capability becomes 100% accurate for the binary classification problem of detecting COVID-19 scans. Source code of this work can be found at GitHub-link .

Matched MeSH terms: Tomography, X-Ray Computed/methods*
Fulltext A Case of Appendiceal Goblet Cell Carcinoid Tumor: Getting it right under the Microscope

Kang WH, Wahab NA, Kamaruddin NA

J ASEAN Fed Endocr Soc, 2020;35(1):102-104.
PMID: 33442175 DOI: 10.15605/jafes.035.01.16

Goblet cell carcinoid (GCC) is a rare neoplasm of the vermiform appendix and can be mistaken as a typical neuroendocrine tumour (TNET). The natural history of this disease is more aggressive compared to TNETs and requires a more aggressive approach. We report a case of a 37-year-old male who was initially diagnosed with TNET, but subsequently revised as Tang's A GCC. He underwent appendectomy and right hemicolectomy. Aside from a persistently elevated carcinoembyrogenic antigen (CEA) result, his 18F-fluorodeoxyglucose (FDG) PET/CT and a 68-Gallium DOTATATE PET/CT scan showed no FDG or DOTATATE avid lesions.

Matched MeSH terms: Positron Emission Tomography Computed Tomography
Fulltext A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction

Rahman H, Khan AR, Sadiq T, Farooqi AH, Khan IU, Lim WH

Tomography, 2023 Dec 05;9(6):2158-2189.
PMID: 38133073 DOI: 10.3390/tomography9060169

Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.

Matched MeSH terms: Tomography, X-Ray Computed/methods
Exploring age and gender variations in root canal morphology of maxillary premolars in Saudi sub population: a cross-sectional CBCT study

Karobari MI, Iqbal A, Batul R, Adil AH, Syed J, Algarni HA, et al.

BMC Oral Health, 2024 May 09;24(1):543.
PMID: 38724952 DOI: 10.1186/s12903-024-04310-w

BACKGROUND: In complex teeth like maxillary premolars, endodontic treatment success depends on a complete comprehension of root canal anatomy. The research on mandibular premolars' root canal anatomy has been extensive and well-documented in existing literature. However, there appears to be a notable gap in available data concerning the root canal anatomy of maxillary premolars. This study aimed to explore the root canal morphology of maxillary premolars using cone-beam computed tomography (CBCT) imaging, considering age and gender variations.
METHODS: From 500 patient CBCT scans, 787 maxillary premolar teeth were evaluated. The sample was divided by gender and age (10-20, 21-30, 31-40, 41-50, 51-60, and 61 years and older). Ahmed et al. classification system was used to record root canal morphology.
RESULTS: The most frequent classifications for right maxillary 1st premolars were 2MPM1 B1 L1 (39.03%) and 1MPM1 (2.81%), while the most frequent classifications for right maxillary 2nd premolars were 2MPM1 B1 L1 (39.08%) and 1MPM1 (17.85%). Most of the premolars typically had two roots (left maxillary first premolars: 81.5%, left maxillary second premolars: 82.7%, right maxillary first premolars: 74.4%, right maxillary second premolars: 75.7%). Left and right maxillary 1st premolars for classes 1MPM1 and 1MPM1-2-1 showed significant gender differences. For classifications 1MPM1 and 1MPM1-2-1, age-related changes were seen in the left and right maxillary first premolars.
CONCLUSION: This study provides novel insights into the root canal anatomy of maxillary premolars within the Saudi population, addressing a notable gap in the literature specific to this demographic. Through CBCT imaging and analysis of large sample sizes, the complex and diverse nature of root canal morphology in these teeth among Saudi individuals is elucidated. The findings underscore the importance of CBCT imaging in precise treatment planning and decision-making tailored to the Saudi population. Consideration of age and gender-related variations further enhances understanding and aids in personalized endodontic interventions within this demographic.

Matched MeSH terms: Cone-Beam Computed Tomography*
Effects of rapid maxillary expansion on upper airway parameters in OSA children with maxillary restriction: A CBCT study

Zreaqat M, Hassan R, Alforaidi S, Kassim NK

Pediatr Pulmonol, 2024 Oct;59(10):2490-2498.
PMID: 38771201 DOI: 10.1002/ppul.27050

BACKGROUND: Rapid maxillary expansion (RME) has been proposed as an effective treatment for pediatric obstructive sleep apnea (OSA) and maxillary restriction in children. This study aimed to evaluate the effect of RME appliances on the nasomaxillary complex dimensions in children with OSA and maxillary constriction.
METHODS: This prospective longitudinal study included 34 children aged 8-12 years with maxillary restriction and OSA confirmed by polysomnography who had completed RME therapy. The nasomaxillary complex is segmented into the nasal cavity, maxillary sinuses, and nasopharynx. The effect of RME on nasomaxillary complex dimensions was assessed pre and posttreatment using cone-beam computed tomography, analysis, while a second standard overnight polysomnography (PSG) was performed to assess changes in respiratory parameters.
RESULTS: Significant improvements were observed, including inferior maxillary dislocation (S-S1 distance and N-ANS), increased anterior and posterior facial height, and a 5.43 events/h reduction in Apnea-Hypopnea Index (p

Matched MeSH terms: Cone-Beam Computed Tomography*
Fulltext Stroke thrombolysis at 5.5 hours based on computed tomography perfusion

Sahathevan R, Azmin S, Palaniappan S, Nafisah WY, Tan HJ, Norlinah MI, et al.

Malays J Med Sci, 2014 Mar;21(2):78-81.
PMID: 24876813 MyJurnal

A young man was admitted with sudden onset of right-sided weakness. He was assessed in the emergency department, and an immediate computed tomography (CT) perfusion study of the brain was arranged, which showed a left middle cerebral artery territory infarct with occlusion of the M1 segment. There was a significant penumbra measuring approximately 50% of the arterial territory. By the time his assessment was completed, it was 5.5 hours from the onset of symptoms. He was nonetheless administered intravenous recombinant tissue plasminogen activator (rtPA) based on the significant penumbra. He was discharged from the hospital after one week with significant residual deficit. At 2 months clinic follow-up, he showed almost complete recovery with a Modified Rankin Score of 1. We hope to demonstrate that a significant penumbra is an important determinant for good neurological recovery and outcome following stroke thrombolysis, even when patients present outside the 4.5 hours onset-to-treatment time window.

Matched MeSH terms: Tomography; Tomography, X-Ray Computed
Fulltext Computed Tomography (CT) Imaging of Injuries from Blunt Abdominal Trauma: A Pictorial Essay

Hassan R, Abd Aziz A

Malays J Med Sci, 2010 Apr;17(2):29-39.
PMID: 22135535 MyJurnal

Blunt abdominal trauma can cause multiple internal injuries. However, these injuries are often difficult to accurately evaluate, particularly in the presence of more obvious external injuries. Computed tomography (CT) imaging is currently used to assess clinically stable patients with blunt abdominal trauma. CT can provide a rapid and accurate appraisal of the abdominal viscera, retroperitoneum and abdominal wall, as well as a limited assessment of the lower thoracic region and bony pelvis. This paper presents examples of various injuries in trauma patients depicted in abdominal CT images. We hope these images provide a resource for radiologists, surgeons and medical officers, as well as a learning tool for medical students.

Matched MeSH terms: Tomography; Tomography, X-Ray Computed
Fulltext Recurrent Intestinal Obstruction in a Patient with Selective IgA Deficiency

Hui CK

Malays J Med Sci, 2016 Nov;23(6):123-127.
PMID: 28090187 DOI: 10.21315/mjms2016.23.6.14

A 32 year old woman presented with acute onset of abdominal pain and fever. An urgent computerised tomography (CT) of the whole abdomen showed dilated loop at the terminal ileum in the right lower abdomen with thickening of the wall and oedema. The CT was suggestive of distal small bowel obstruction at the ileum with surrounding wall oedema. Multiple biopsies taken from the terminal ileum and colon on colonoscopy were all unremarkable. She represented one-year later with a recurrence of intestinal obstruction. CT enteroclysis showed collapse at the distal 3 cm segment of the terminal ileum. There was no associated wall thickening, active inflammatory changes or ileitis. This was suspicious of post-inflammatory change or fibrosis. She was subsequently found to have selective IgA deficiency with recurrent infection in the terminal ileum resulting in intestinal obstruction. In conclusion, selective IgA deficiency should be considered in patients with recurrent intestinal obstruction without anatomical obstructions.

Matched MeSH terms: Tomography; Tomography, X-Ray Computed
Fulltext Accuracy of Computed Tomography Attenuation Value of Lumbar Vertebra to Assess Bone Mineral Density

Chia KK, Haron J, Nik Malek NFS

Malays J Med Sci, 2021 Feb;28(1):41-50.
PMID: 33679219 DOI: 10.21315/mjms2021.28.1.6

Background: Computed tomography (CT) attenuation (Hounsfield unit [HU]) value of lumbar vertebra may provide an alternative method in the detection of osteoporosis during CT scans.
Methods: A cross-sectional study on 50 patients of age 50 and above with contrast-enhanced CT (CECT) and dual-energy X-ray absorptiometry (DXA) was conducted from November 2018 to November 2019. Single region of interest (ROI) was placed at the anterior trabecular part of L1 vertebra on CECT to obtain HU value. Correlation of CT HU value of L1 vertebra and DXA T-score, interrater reliability agreement between HU value of L1 vertebra and T-score in determining groups of with and without osteoporosis, ROC curve analysis for diagnostic accuracy and cut-off value of CT for detection of osteoporosis were identified.
Results: Significant correlation between HU value of L1 vertebra and L1 T-score (r = 0.683)/lowest skeletal T-score (r = 0.703) (P < 0.001). Substantial agreement between HU value of L1 vertebra and DXA in determining the groups with and without osteoporosis (k = 0.8; P < 0.001). The area under the receiver operating characteristic (AUROC) curve was 0.93 (95% CI: 0.86, 1.00) using HU value (P < 0.001). Cut-off value for osteoporosis was 149 HU.
Conclusion: HU value of lumbar vertebra is an effective alternative for the detection of osteoporosis with high diagnostic accuracy in hospitals without DXA facility.

Matched MeSH terms: Tomography; Tomography, X-Ray Computed
Fulltext Isolated sphenoid sinus lesion: A diagnostic dilemma

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
Fulltext Computed Tomography (CT) of Blunt Abdominal Trauma: The Frequency of Incidental Findings, how it was Documented in Radiology Report and the Implication of These Findings to Acute Trauma Care

Radhiana H, Siti Kamariah CM, Mohd Nazli K, Azian AA

Med J Malaysia, 2014 Feb;69(1):46-8.
PMID: 24814633 MyJurnal

The wide use of computed tomography (CT) scanning for patients with blunt abdominal trauma can reveal incidental findings that vary in their importance. We evaluated these findings, how it was reported by radiologists and its implication on the trauma care. In 30 out of 154 patients, 32 incidental findings were discovered (19.5%). Out of these 32 findings, only 3 cases (9.4%) were considered significant and required immediate attention from the managing team. In all these 3 cases, the findings were described in the body of the report and highlighted in the conclusion section at the end of the radiology report. However, similar reporting style was used in only 58.4% of cases with moderate clinical concern and 23.5% of cases with little clinical concern. In 41.2% of cases with little concern, the incidental findings were not mentioned in the radiology report. In conclusion, incidental findings in CT scan performed for blunt abdominal trauma were common but many were clinically insignificant. There is little consistency in radiology reporting of these findings especially those with moderate and little clinical concern.

Matched MeSH terms: Tomography, X-Ray Computed
Fulltext Papillary thyroid carcinoma presenting with intraluminal tracheal mass symptoms

Mohamad I, Haron A

Med J Malaysia, 2013 Apr;68(2):164-5.
PMID: 23629566 MyJurnal

Papillary thyroid carcinoma is a common thyroid malignancy reported world wide. It affects females more commonly in the 4th to 6th decades of life. The patients usually present with a painless anterior neck mass and occasionally with lymph node involvement. We report a case of an elderly male who presented with hoarseness and hemoptysis, which warranted bronchoscopy. Biopsy of the intraluminal tracheal mass revealed the diagnosis of papillary thyroid carcinoma. Computed tomography scan of the neck confirmed the presence of the primary lesion in the right thyroid lobe with invasion into the adjacent trachea and esophagus.

Matched MeSH terms: Tomography, X-Ray Computed
Fulltext A rare case of an appendiceal mass masquerading as a pelvic tumour and causing bilateral hydronephrosis

Abdul Rashid S, Ab Hamid S, Mohamad Saini S, Muridan R

Biomed Imaging Interv J, 2012 Apr;8(2):e11.
PMID: 22970067 MyJurnal DOI: 10.2349/biij.8.2.e11

Diagnosing acute appendicitis in children can be difficult due to atypical presenting symptoms. While there are reported cases of acute appendicitis or appendiceal masses causing unilateral hydronephrosis, bilateral hydronephrosis as a complication of appendiceal mass is very rare. We report a case of a child who presented with cardinal symptomatology associated with the urogenital tract. Ultrasound (US) investigation showed a pelvic mass causing bilateral hydronephrosis. An initial diagnosis of a pelvic teratoma was made based on the US and computed tomography (CT) scan findings. The final diagnosis of an appendiceal mass causing bilateral hydronephrosis was established intraoperatively.

Matched MeSH terms: Tomography, X-Ray Computed
Fulltext Pelvic aneurysmal bone cyst

Sharifah M, Nurhazla H, Suraya A, Tan S

Biomed Imaging Interv J, 2011 Oct;7(4):e24.
PMID: 22279501 MyJurnal DOI: 10.2349/biij.7.4.24

This paper describes an extremely rare case of a huge aneurysmal bone cyst (ABC) in the pelvis, occurring in the patient's 5(th) decade of life. The patient presented with a history of painless huge pelvic mass for 10 years. Plain radiograph and computed tomography showed huge expansile lytic lesion arising from the right iliac bone. A biopsy was performed and histology confirmed diagnosis of aneurysmal bone cyst. Unfortunately, the patient succumbed to profuse bleeding from the tumour.

Matched MeSH terms: Tomography, X-Ray Computed
A missed bilateral choanal atresia

Irfan M, Suzina SA

Ann Acad Med Singap, 2010 Jan;39(1):72.
PMID: 20126823
Matched MeSH terms: Tomography, X-Ray Computed
Fulltext Imaging as an aid to the diagnosis of acute appendicitis

Wijesuriya LI

Malays Fam Physician, 2007;2(3):106-9.
PMID: 25606095 MyJurnal

Acute appendicitis has been known as a disease entity for well over a century but a confident diagnosis before surgery in all patients suspected of the condition is still not possible. Timely diagnosis is essential to minimise morbidity due to possible perforation of the inflamed organ in the event treatment is delayed; so much so that surgeons often preferred to operate at the slightest suspicion of the diagnosis in the past. This resulted in the removal of many normal appendixes. When the diagnosis of appendicitis is clear from the history and clinical examination, then no further investigation is necessary and prompt surgical treatment is appropriate. Where there is doubt about the diagnosis however it is advisable to resort to imaging studies such as abdominal ultrasound or computed tomography to clear such suspicions before subjecting the patient to an appendicectomy. These studies would also help avoid delays in surgery in deserving patients.

Matched MeSH terms: Tomography

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