How to perform an ultrasound to diagnose endometriosis
Ontology highlight
ABSTRACT:
SUBMITTER: Leonardi M
PROVIDER: S-EPMC8409846 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:"How to perform contrast-enhanced ultrasound (CEUS)" provides general advice on the use of ultrasound contrast agents (UCAs) for clinical decision-making and reviews technical parameters for optimal CEUS performance. CEUS techniques vary between centers, therefore, experts from EFSUMB, WFUMB and from the CEUS LI-RADS working group created a discussion forum to standardize the CEUS examination technique according to published evidence and best personal experience. The goal is to standardise the use and administration of UCAs to facilitate correct diagnoses and ultimately to improve the management and outcomes of patients.
| S-EPMC5802984 | biostudies-literature
Project description: Not available
| S-EPMC7258635 | biostudies-literature
Project description:With the improvement in survival rates of low-birthweight and very premature infants, neonatal fungal infection, especially fungal pneumonia, is becoming more and more common, but the diagnosis is always challenging. Recently, lung ultrasound (LUS) has been used to diagnose pneumonia in newborn infants, but not fungal pneumonia. This paper summarizes the ultrasonographic features of seven cases of neonatal fungal pneumonia, such as lung consolidation with air bronchograms, shred signs, lung pulse, pleural line abnormalities, and different kinds of B-lines. It was confirmed that LUS plays an important role in the diagnosis of fungal pneumonia in newborn infants.
| S-EPMC9331912 | biostudies-literature
Project description:Pneumomediastinum is a relatively rare and usually a self-limiting condition in infants. However, it can lead to tension pneumomediastinum or pneumothorax, which may rapidly become life-threatening. Therefore, its timely and accurate detection is important and close follow-up to resolution is needed. Ultrasound can be used as a real-time problem solver to accurately diagnose pneumomediastinum in an infant and should be encouraged as a radiation-free adjunct imaging modality when radiography cannot provide a definite diagnosis. We present a case of a 13-month-old preterm girl with bronchopulmonary dysplasia and acute respiratory infection who presented with sudden respiratory failure due to spontaneous pneumomediastinum. Pneumomediastinum was eventually diagnosed with ultrasound, after chest radiography performed during the on-call hours was misinterpreted because not all typical radiographic signs of pneumomediastinum were present.
| S-EPMC8421572 | biostudies-literature
Project description:ObjectivesPouch of Douglas (POD) obliteration is a severe consequence of inflammation in the pelvis, often seen in patients with endometriosis. The sliding sign is a dynamic transvaginal ultrasound (TVS) test that can diagnose POD obliteration. We aimed to develop a deep learning (DL) model to automatically classify the state of the POD using recorded videos depicting the sliding sign test.MethodsTwo expert sonologists performed, interpreted, and recorded videos of consecutive patients from September 2018 to April 2020. The sliding sign was classified as positive (i.e. normal) or negative (i.e. abnormal; POD obliteration). A DL model based on a temporal residual network was prospectively trained with a dataset of TVS videos. The model was tested on an independent test set and its diagnostic accuracy including area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive value (PPV/NPV) was compared to the reference standard sonologist classification (positive or negative sliding sign).ResultsIn a dataset consisting of 749 videos, a positive sliding sign was depicted in 646 (86.2%) videos, whereas 103 (13.8%) videos depicted a negative sliding sign. The dataset was split into training (414 videos), validation (139), and testing (196) maintaining similar positive/negative proportions. When applied to the test dataset using a threshold of 0.9, the model achieved: AUC 96.5% (95% CI: 90.8-100.0%), an accuracy of 88.8% (95% CI: 83.5-92.8%), sensitivity of 88.6% (95% CI: 83.0-92.9%), specificity of 90.0% (95% CI: 68.3-98.8%), a PPV of 98.7% (95% CI: 95.4-99.7%), and an NPV of 47.7% (95% CI: 36.8-58.2%).ConclusionsWe have developed an accurate DL model for the prediction of the TVS-based sliding sign classification.Lay summaryEndometriosis is a disease that affects females. It can cause very severe scarring inside the body, especially in the pelvis - called the pouch of Douglas (POD). An ultrasound test called the 'sliding sign' can diagnose POD scarring. In our study, we provided input to a computer on how to interpret the sliding sign and determine whether there was POD scarring or not. This is a type of artificial intelligence called deep learning (DL). For this purpose, two expert ultrasound specialists recorded 749 videos of the sliding sign. Most of them (646) were normal and 103 showed POD scarring. In order for the computer to interpret, both normal and abnormal videos were required. After providing the necessary inputs to the computer, the DL model was very accurate (almost nine out of every ten videos was correctly determined by the DL model). In conclusion, we have developed an artificial intelligence that can interpret ultrasound videos of the sliding sign that show POD scarring that is almost as accurate as the ultrasound specialists. We believe this could help increase the knowledge on POD scarring in people with endometriosis.
| S-EPMC8801033 | biostudies-literature
Project description:Perineal and rectal pain are common presentations in the emergency department (ED). In the majority of cases, clinical examination is sufficient to detect local anorectal pathologies. However, perianal and rectal abscesses and fistulas are often the primary concerns prompting diagnostic imaging in the ED. Currently, computed tomography is the preferred imaging modality. Recently, transperineal ultrasound has emerged as an optimal imaging modality for the diagnosis of perineal and perianal abscesses. We present a case in which point-of-care ultrasound accurately detected an intersphincteric abscess, and review the appropriate ultrasound technique to evaluate patients with suspected perianal and rectal abscesses.
| S-EPMC6861055 | biostudies-literature
Project description:BackgroundLung ultrasound is a useful tool in the assessment of pulmonary congestion in heart failure that is typically performed and interpreted by physicians at the point-of-care.AimsTo investigate the ability of nurses, students, and paramedics to accurately identify B-lines and pleural effusions for the detection of pulmonary congestion in heart failure and to examine the training necessary.Methods and resultsWe conducted a systematic review and searched online databases for studies that investigated the ability of nurses, students, and paramedics to perform lung ultrasound and detect B-lines and pleural effusions. Of 979 studies identified, 14 met our inclusion criteria: five in nurses, eight in students, and one in paramedics. After 0-12 h of didactic training and 58-62 practice lung ultrasound examinations, nurses were able to identify B-lines and pleural effusions with a sensitivity of 79-98% and a specificity of 70-99%. In image adequacy studies, medical students with 2-9 h of training were able to acquire adequate images for B-lines and pleural effusions in 50-100%. Only one eligible study investigated paramedic-performed lung ultrasound which did not support the ability of paramedics to adequately acquire and interpret lung ultrasound images after 2 h of training.ConclusionsOur findings suggest that nurses and students can accurately acquire and interpret lung ultrasound images after a brief training period in a majority of cases. The examination of heart failure patients with lung ultrasound by non-clinicians appears feasible and warrants further investigation.
| S-EPMC6919316 | biostudies-literature
Project description: Not available
| S-EPMC7264804 | biostudies-literature
Project description:PurposeThe preoperative diagnosis of endometriosis associated ovarian cancer (EAOC) remains challenging for lack of effective diagnostic biomarker. We aimed to study clinical characteristics and develop a nomogram for diagnosing EAOC before surgery.MethodsA total of 87 patients with EAOC and 348 patients with ovarian endometrioma (OEM) were enrolled in our study. Least absolute shrinkage and selection operator (LASSO) regression and Logistic regression were utilized to select variables and construct the prediction model. The performance of the model was assessed using receiver operating characteristic (ROC) analyses and calibration plots, while decision curve analyses (DCAs) were conducted to assess clinical value. Bootstrap resampling was used to evaluated the stability of the model in the derivation set.ResultsThe EAOC patients were older compared to the OEM patients (46.41 ± 9.62 vs. 36.49 ± 8.09 year, P < 0.001) and proportion of postmenopausal women was higher in EAOC group than in the OEM group (34.5 vs. 1.5%, P < 0.001). Our prediction model, which included age at diagnosis, tumor size, cancer antigen (CA) 19-9 and risk of ovarian malignancy algorithm (ROMA), demonstrated an area under the curve (AUC) of 0.858 (95% confidence interval (CI): 0.795-0.920) in the derivation set (N = 304) and an AUC of 0.870 (95% CI: 0.779-0.961) in the validation set (N = 131). The model fitted both the derivation (Hosmer-Lemeshow test (HL) chi-square = 12.600, P = 0.247) and the validation (HL chi-square = 8.210, P = 0.608) sets well.ConclusionCompared to patients with OEM, those with EAOC exhibited distinct clinical characteristics. Our four-variable prediction model demonstrated excellent performance in both the derivation and validation sets, suggesting its potential to assist with preoperative diagnosis of EAOC.
| S-EPMC10799100 | biostudies-literature
Project description:Objectives: This pilot study aims to determine if patients untrained in performing ultrasound can self-scan to obtain images under remote clinician teleguidance during a simulated telehealth encounter. This study also seeks to describe the patients' comfort level and barriers to performing an ultrasound examination on themselves using a handheld ultrasound device. Methods: This was a single center prospective observational cohort study conducted over a 4-month period in 2021. Patients were eligible if they had no prior training in the use of ultrasound and in the use of teleguidance. They voluntarily consented to participate at a single ambulatory internal medicine clinic. Results: 20 participants were enrolled and underwent teleguidance to ultrasound their own skin and soft tissues at the antecubital fossae. Six second video clips were evaluated by 2 subject matter experts using the Point of Care Ultrasound Image Quality scale. A score >7 was considered adequate for diagnostic interpretation. The average score was 10.15/14, with a minimum score of 5/14, and maximum score of 14/14 and a standard deviation (SD) of 2.39 using a two tailed Z-score. Setting alpha at 0.05 the 95% CI was (5.47-14.83). Conclusion: In a pilot study of 20 participants with no ultrasound experience, untrained healthy volunteers were able to perform technically acceptable and interpretable ultrasound scans using teleguidance by a trained clinician.
| S-EPMC10721299 | biostudies-literature