Anatomical variants and pathologies of the vermix

Deshmukh, Swati; Verde, Franco; Johnson, Pamela T.; Fishman, Elliot K.; Macura, Katarzyna J.

doi:10.1007/s10140-014-1206-4

Cited by 40 publications

(47 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…www.nature.com/scientificreports www.nature.com/scientificreports/ Moreover, the appendix may only be present on a few images of an entire exam that can comprise up to several hundred images depending on the slice thickness leading to class imbalance. Other contributory challenges include overlap in CT appearance of normal and abnormal appendices 39 , varied location within the body 40 , the myriad image manifestations of an inflamed appendix, and non-visualization of the normal appendix in 43-58% of CT examinations 41 . The performance of our 3D CNN for automated detection of acute appendicitis with high diagnostic performance of 0.810 (95% CI 0.725, 0.895 is comparable to previously published 3D CNN models for automated detection of emergent findings on CT 42 .…”

Section: Discussionmentioning

confidence: 99%

AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining

Rajpurkar

Park

Irvin

et al. 2020

Sci Rep

View full text Add to dashboard Cite

The development of deep learning algorithms for complex tasks in digital medicine has relied on the availability of large labeled training datasets, usually containing hundreds of thousands of examples. The purpose of this study was to develop a 3D deep learning model, AppendiXNet, to detect appendicitis, one of the most common life-threatening abdominal emergencies, using a small training dataset of less than 500 training CT exams. We explored whether pretraining the model on a large collection of natural videos would improve the performance of the model over training the model from scratch. AppendiXNet was pretrained on a large collection of YouTube videos called Kinetics, consisting of approximately 500,000 video clips and annotated for one of 600 human action classes, and then fine-tuned on a small dataset of 438 CT scans annotated for appendicitis. We found that pretraining the 3D model on natural videos significantly improved the performance of the model from an AUC of 0.724 (95% CI 0.625, 0.823) to 0.810 (95% CI 0.725, 0.895). The application of deep learning to detect abnormalities on CT examinations using video pretraining could generalize effectively to other challenging cross-sectional medical imaging tasks when training data is limited.Appendicitis is one of the most common life-threatening abdominal emergencies, with lifetime risk of ranging from 5-10% and an annual incidence of 90-140 per 100,000 individuals 1-3 . Treatment, via appendectomy, remains the most frequently performed surgical intervention in the world 4 . Computed tomography (CT) imaging of the abdomen is the primary imaging modality to make the diagnosis of appendicitis, exclude other etiologies of acute abdominal pain, and allow for surgical planning and intervention. Because a delay in the time between CT imaging diagnosis and surgical appendectomy can worsen patient outcomes 5-9 , there is increasing pressure on hospital systems to provide 24-7 access to advanced imaging and to ensure that the results of urgent findings, such as appendicitis, are rapidly and accurately communicated to the referring physician 10,11 . However, providing rapid and accurate diagnostic imaging is increasingly difficult to sustain for many medical systems and radiology groups as utilization has rapidly expanded; for example, the CT utilization rate in the emergency room increased from 41 per 1000 in 2000 to 74 per 1000 in 2010, with abdominal CTs accounting for over half of this volume 12,13 . Development of automated systems could potentially help improve diagnostic accuracy and reduce time to diagnosis, thereby improving the quality and efficiency of patient care.Recent advancements in deep learning have enabled algorithms to automate a wide variety of medical tasks [14][15][16][17][18][19][20] . A key aspect for the success of deep learning models on these tasks is the availability of large labeled datasets of medical images 21 , usually containing hundreds of thousands of examples. However, it is challenging to curate large labeled medical imaging dat...

show abstract

Section: Discussionmentioning

confidence: 99%

AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining

Rajpurkar

Park

Irvin

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…On MRI, the appendix is a blind-ending tubular structure and about 9-10 cm in length [12,13]. The diameter of normal appendix should be less than 7 mm with wall thickness less than 2 mm [14].…”

Section: Discussionmentioning

confidence: 99%

MRI for Suspected Appendicitis in Pregnancy

CH¹,

YR²,

YT³

2017

J Med Diagn Meth

View full text Add to dashboard Cite

Aim: Acute appendicitis is the most common disease in pregnant women. Based on the premise of the protection of fetus, diagnosis sometimes would be difficult. The purpose of this study is to evaluate the risk and benefit of magnetic resonance imaging for suspected acute appendicitis in Pregnancy.Method: From Oct 2015 to Apr 2017, 10 pregnant women came to our emergency department because of suspected acute appendicitis. We bring MRI to participate the diagnosis in case ultrasound is insufficient to make accurate detection.Result: 5 cases with nonvisualization of appendicitis and 5 cases were confirmed acute appendicitis. Ultrasound of these women couldn't clearly show the abnormal anatomy. But with non-radiation MRI acute appendicitis with presence of appendicoliths were identified. Conclusion:The use of MRI in pregnant women with suspected acute appendicitis is meaningful, not only in confirmation or exclusion of appendicitis but also alternative non-radiation diagnosis tool for further treatment.

show abstract

“…The body is a thin, tubular part between the base and the tip, which is the distal blind end. The appendix may be filled with feces, air, or contrast material (Figure 1) [4].…”

Section: Normal Imaging Appearancementioning

confidence: 99%

Imaging Findings of the Unusual Presentations, Associations and Clinical Mimics of Acute Appendicitis

Demir¹,

Savas²,

Furuncuoglu³

et al. 2017

Eurasian J Med

View full text Add to dashboard Cite

There are many kinds of unusual presentations or associations and clinical mimics of acute appendicitis, and definitive diagnosis requires knowledge of the imaging findings in some cases. The unusual presentations and associations of acute appendicitis included in this study are perforated appendicitis, acute appendicitis occurring in hernias, acute appendicitis with cystic endosalpingiosis, intussusception of appendix, and acute appendicitis with pregnancy. We also present uncommon gastrointestinal, urinary and gynecologic clinical mimics of acute appendicitis including anomalous congenital band, duplication cysts, giant Meckel' s diverticulitis, inflammatory fibroid polyp, renal artery thrombosis, spontaneous urinary extravasation and OHVIRA syndrome. Familiarity with these entities may improve diagnostic accuracy and enable the quickest and most appropriate clinical management.

show abstract

Anatomical variants and pathologies of the vermix

Cited by 40 publications

References 16 publications

AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining

AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining

MRI for Suspected Appendicitis in Pregnancy

Imaging Findings of the Unusual Presentations, Associations and Clinical Mimics of Acute Appendicitis

Contact Info

Product

Resources

About