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BJOG : An International Journal of... Dec 2021To assess the general population's knowledge regarding the utility and availability of tools to diagnosis endometriosis, with a focus on ultrasound.
OBJECTIVE
To assess the general population's knowledge regarding the utility and availability of tools to diagnosis endometriosis, with a focus on ultrasound.
DESIGN
An international cross-sectional online survey study was performed between August and October 2019.
SETTING AND POPULATION
5301 respondents, representing 73 countries.
METHODS
In all, 23 questions survey focused on knowledge of endometriosis diagnosis distributed globally via patient- and community-endometriosis groups using social media.
MAIN OUTCOMES AND MEASURES
Descriptive data of the knowledge of diagnostic tools for diagnosing endometriosis, including details about diagnosis using ultrasound.
RESULTS
In all, 84.0% of respondents had been previously diagnosed with endometriosis, 71.5% of whom had been diagnosed at the time of surgery. Ultrasound and MRI were the methods of diagnosis in 6.5% and 1.8%, respectively. A total of 91.8%, 28.8% and 16.6% of respondents believed surgery, ultrasound and MRI could diagnose endometriosis, respectively (more than one answer allowed). In those diagnosed by surgery, 21.7% knew about ultrasound as a diagnosis method, whereas in those diagnosed non-surgically, 51.5% knew (P < 0.001). In all, 14.7%, 31.1% and 18.2% stated superficial, ovarian and deep endometriosis could be diagnosed with ultrasound (32.9% stated they did not know which phenotypes of endometriosis could be diagnosed). Lastly, 58.4% of respondents do not believe they could access an advanced ultrasound in their region.
CONCLUSIONS
There is a limited appreciation for the role of non-surgical diagnostic tests for endometriosis among lay respondents to this survey.
TWEETABLE ABSTRACT
International survey shows limited awareness of lay respondents about non-surgical endometriosis diagnostic tools.
Topics: Adult; Cross-Sectional Studies; Endometriosis; Female; Health Knowledge, Attitudes, Practice; Humans; Magnetic Resonance Imaging; Middle Aged; Pelvic Pain; Surveys and Questionnaires; Ultrasonography
PubMed: 34403184
DOI: 10.1111/1471-0528.16865 -
Fukushima Journal of Medical Science Aug 2017Endoscopic ultrasonography (EUS) plays a major role in diagnosing gallbladder (GB) cancer and pancreatic cancer (PC). In cases of GB cancer, EUS allows for precise... (Review)
Review
Endoscopic ultrasonography (EUS) plays a major role in diagnosing gallbladder (GB) cancer and pancreatic cancer (PC). In cases of GB cancer, EUS allows for precise observations of morphology and wall layers. However, proficiency is required for the morphologic diagnosis of GB tumors. Therefore, contrast-enhanced harmonic EUS (CH-EUS) began to be performed to diagnose GB lesions. CH-EUS enables real-time observation of the hemodynamics of GB tumors. The enhanced patterns generated by CH-EUS improve precision in the diagnosis of such tumors.PC appears as a hypoechoic mass on EUS. However, distinguishing between PC and mass-forming pancreatitis or focal autoimmune pancreatitis (AIP) is difficult via conventional EUS. CH-EUS allows for differentiating among these diseases (PC is hypoenhanced and heterogeneously enhanced, pancreatitis is isoenhanced, and a pancreatic neuroendocrine tumor is hyperenhanced). EUS-guided fine needle aspiration (EUS-FNA) also contributes to pathological diagnoses of pancreatic lesions. However, certain PC patients cannot be diagnosed via EUS-FNA. PC is heterogeneously enhanced on CH-EUS, and unenhanced regions have been reported to be areas of fibrosis or necrosis. CH-EUS-guided fine needle aspiration (CH-EUS-FNA) permits puncturing of the enhanced area while avoiding necrotic and fibrotic regions. Moreover, as CH-EUS findings have been quantitatively analyzed, a time-intensity curve (TIC) has become usable for diagnosing solid pancreatic lesions. CH-EUS-related techniques have been developed and increasingly utilized in the pancreaticobiliary area.
Topics: Contrast Media; Endoscopic Ultrasound-Guided Fine Needle Aspiration; Endosonography; Gallbladder Neoplasms; Humans; Image Enhancement; Pancreatic Neoplasms
PubMed: 28680009
DOI: 10.5387/fms.2017-04 -
Mycopathologia Dec 2015Oral candidiasis is one of the most common opportunistic fungal infections of the oral cavity in human. Among children, this condition represents one of the most... (Review)
Review
INTRODUCTION
Oral candidiasis is one of the most common opportunistic fungal infections of the oral cavity in human. Among children, this condition represents one of the most frequent affecting the mucosa. Although most diagnoses are made based on clinical signs and features, a microbiological analysis is sometimes necessary. We performed a literature review on the diagnosis of oral candidiasis to identify the techniques most commonly employed in routine clinical practice.
MATERIALS AND METHODS
A Medline-PubMed search covering the last 10 years was performed.
RESULTS
Microbiological techniques were used in cases requiring confirmation of the clinical diagnosis. In such cases, direct microscopy was the method most commonly used for diagnosing candidiasis.
CONCLUSION
Direct microscopy appears as the method of choice for confirming clinical diagnosis and could become a routine chair-side technique.
Topics: Adolescent; Candidiasis, Oral; Child; Child, Preschool; Humans; Microscopy
PubMed: 26329143
DOI: 10.1007/s11046-015-9933-y -
BMC Endocrine Disorders Jun 2023To compare the ability of the Cox regression and machine learning algorithms to predict the survival of patients with Anaplastic thyroid carcinoma (ATC).
BACKGROUND
To compare the ability of the Cox regression and machine learning algorithms to predict the survival of patients with Anaplastic thyroid carcinoma (ATC).
METHODS
Patients diagnosed with ATC were extracted from the Surveillance, Epidemiology, and End Results database. The outcomes were overall survival (OS) and cancer-specific survival (CSS), divided into: (1) binary data: survival or not at 6 months and 1 year; (2): time-to-event data. The Cox regression method and machine learnings were used to construct models. Model performance was evaluated using the concordance index (C-index), brier score and calibration curves. The SHapley Additive exPlanations (SHAP) method was deployed to interpret the results of machine learning models.
RESULTS
For binary outcomes, the Logistic algorithm performed best in the prediction of 6-month OS, 12-month OS, 6-month CSS, and 12-month CSS (C-index = 0.790, 0.811, 0.775, 0.768). For time-event outcomes, traditional Cox regression exhibited good performances (OS: C-index = 0.713; CSS: C-index = 0.712). The DeepSurv algorithm performed the best in the training set (OS: C-index = 0.945; CSS: C-index = 0.834) but performs poorly in the verification set (OS: C-index = 0.658; CSS: C-index = 0.676). The brier score and calibration curve showed favorable consistency between the predicted and actual survival. The SHAP values was deployed to explain the best machine learning prediction model.
CONCLUSIONS
Cox regression and machine learning models combined with the SHAP method can predict the prognosis of ATC patients in clinical practice. However, due to the small sample size and lack of external validation, our findings should be interpreted with caution.
Topics: Humans; Thyroid Carcinoma, Anaplastic; Algorithms; Databases, Factual; Machine Learning; Thyroid Neoplasms; Prognosis
PubMed: 37291551
DOI: 10.1186/s12902-023-01368-5 -
Medical Decision Making : An... May 2016The unconscious thought theory argues that making complex decisions after a period of distraction can lead to better decision quality than deciding either immediately or...
The unconscious thought theory argues that making complex decisions after a period of distraction can lead to better decision quality than deciding either immediately or after conscious deliberation. Two studies have tested this unconscious thought effect (UTE) in clinical diagnosis with conflicting results. The studies used different methodologies and had methodological weaknesses. We attempted to replicate the UTE in medical diagnosis by providing favorable conditions for the effect while maintaining ecological validity. Family physicians (N= 116) diagnosed 3 complex cases in 1 of 3 thinking modes: immediate, unconscious (UT), and conscious (CT). Cases were divided into short sentences, which were presented briefly and sequentially on computer. After each case presentation, the immediate response group gave a diagnosis, the UT group performed a 2-back distraction task for 3 min before giving a diagnosis, and the CT group could take as long as necessary before giving a diagnosis. We found no differences in diagnostic accuracy between groups (P= 0.95). The CT group took a median of 7 s to diagnose, which suggests that physicians were able to diagnose "online," as information was being presented. The lack of a difference between the immediate and UT groups suggests that the distraction had no additional effect on performance. To assess the decisiveness of the evidence of this null result, we computed a Bayes factor (BF01) for the 2 comparisons of interest. We found a BF01of 5.76 for the UT versus immediate comparison and of 3.61 for the UT versus CT comparison. Both BFs provide substantial evidence in favor of the null hypothesis: physicians' diagnoses made after distraction are no better than diagnoses made either immediately or after self-paced deliberation.
Topics: Adult; Aged; Bayes Theorem; Clinical Decision-Making; Diagnosis; Female; Humans; Male; Middle Aged; Models, Psychological; Physicians, Family; Reproducibility of Results; Unconscious, Psychology
PubMed: 25852079
DOI: 10.1177/0272989X15581352 -
Muscle & Nerve Mar 2015The AANEM strongly recommends that EDX procedures be performed by physicians with comprehensive knowledge of neurological and musculoskeletal disorders to assure...
The AANEM strongly recommends that EDX procedures be performed by physicians with comprehensive knowledge of neurological and musculoskeletal disorders to assure accurate interpretation and diagnosis. Individuals without a medical education in neuromuscular disorders and without special training in EDX procedures typically are not qualified to interpret the waveforms generated by NCSs and needle EMGs or to correlate the findings with other clinical information to reach a diagnosis. It is also the AANEM's position that the same physician should directly supervise and interpret the NCSs including those performed by an EDX technician. The AANEM believes that interpreting NCS without performing a focused history and physical and having oversight over the design and performance is inappropriate. Nerve conduction studies performed independent of needle EMG studies may only provide a portion of the information needed to diagnose muscle, nerve root, and most nerve disorders. For this reason, it is the position of the AANEM that, except in unique situations, NCSs and needle EMG should be performed together in a study design determined by a trained neuromuscular physician. There are common diagnoses that depend on performing a needle EMG and combining the needle EMG data with the NCS data. Needle EMG studies are a necessary part of the evaluation in the diagnosis of myopathy, radiculopathy, plexopathy, disorders of the motor neuron, peripheral neuropathies and most disorders of the individual peripheral motor nerves. When the NCS is used on its own without integrating needle EMG findings or when an individual relies solely on a review of NCS data, the results can often be misleading, and important diagnoses will likely be missed. Patients may thus be subjected to incorrect, unnecessary, and potentially harmful treatment interventions. The AANEM is concerned that utilizing only NCSs to make health care decisions provides incomplete diagnostic information, leading to inadequate or inappropriate therapy for some patients and may increase health care costs.
Topics: Clinical Competence; Electrodiagnosis; Electromyography; Humans; Neural Conduction; Neuromuscular Diseases; Societies, Medical; United States
PubMed: 25676356
DOI: 10.1002/mus.24587 -
Transfusion Medicine Reviews Jan 2020Heparin-induced thrombocytopenia (HIT) affects some of the patients exposed to heparin. It is mediated by antibodies that recognize neoepitopes on platelet factor 4... (Review)
Review
Heparin-induced thrombocytopenia (HIT) affects some of the patients exposed to heparin. It is mediated by antibodies that recognize neoepitopes on platelet factor 4 (PF4)/heparin complexes. A HIT diagnosis requires both clinical and laboratory evaluation and remains a challenge. Since many patients develop antibodies in response to heparin, but only a few of them generate anti-PF4/heparin antibodies capable of activating platelets which consequently cause clinical complications, the performance of serologic assays is not enough to diagnose HIT. Functional assays can identify pathogenic antibodies capable of platelet activation, but they are more demanding and their limited availability contributes to the problem of diagnosing HIT. Restricted laboratories usually collect sera of multiple patients to perform functional assays only once or twice a week; hence, a HIT diagnosis can take several days. The use of flow cytometry appears to be a promising alternative in the confirmation of pathogenic anti-PF4/heparin antibodies. Flow cytometric assays detect either activation markers on a healthy donor's platelet surfaces or platelet derived microparticles formed after platelet incubation with a patient's serum. Flow cytometers are readily available in many clinical laboratories, so this technology introduces the possibility of an earlier HIT diagnosis. The objective of this review was to collect findings on flow cytometric HIT confirmations to the present date, and to review the currently available flow cytometric assays used in the diagnosis of HIT.
Topics: Diagnosis, Differential; Flow Cytometry; Heparin; Humans; Platelet Activation; Thrombocytopenia
PubMed: 31575433
DOI: 10.1016/j.tmrv.2019.08.001 -
CJEM Oct 2023Prompt diagnosis of acute coronary syndrome (ACS) using a 12-lead electrocardiogram (ECG) is a critical task for emergency physicians. While computerized algorithms for...
OBJECTIVES
Prompt diagnosis of acute coronary syndrome (ACS) using a 12-lead electrocardiogram (ECG) is a critical task for emergency physicians. While computerized algorithms for ECG interpretation are limited in their accuracy, machine learning (ML) models have shown promise in several areas of clinical medicine. We performed a systematic review to compare the performance of ML-based ECG analysis to clinician or non-ML computerized ECG interpretation in the diagnosis of ACS for emergency department (ED) or prehospital patients.
METHODS
We searched Medline, Embase, Cochrane Central, and CINAHL databases from inception to May 18, 2022. We included studies that compared ML algorithms to either clinicians or non-ML based software in their ability to diagnose ACS using only a 12-lead ECG, in adult patients experiencing chest pain or symptoms concerning for ACS in the ED or prehospital setting. We used QUADAS-2 for risk of bias assessment. Prospero registration CRD42021264765.
RESULTS
Our search yielded 1062 abstracts. 10 studies met inclusion criteria. Five model types were tested, including neural networks, random forest, and gradient boosting. In five studies with complete performance data, ML models were more sensitive but less specific (sensitivity range 0.59-0.98, specificity range 0.44-0.95) than clinicians (sensitivity range 0.22-0.93, specificity range 0.63-0.98) in diagnosing ACS. In four studies that reported it, ML models had better discrimination (area under ROC curve range 0.79-0.98) than clinicians (area under ROC curve 0.67-0.78). Heterogeneity in both methodology and reporting methods precluded a meta-analysis. Several studies had high risk of bias due to patient selection, lack of external validation, and unreliable reference standards for ACS diagnosis.
CONCLUSIONS
ML models have overall higher discrimination and sensitivity but lower specificity than clinicians and non-ML software in ECG interpretation for the diagnosis of ACS. ML-based ECG interpretation could potentially serve a role as a "safety net", alerting emergency care providers to a missed acute MI when it has not been diagnosed. More rigorous primary research is needed to definitively demonstrate the ability of ML to outperform clinicians at ECG interpretation.
Topics: Adult; Humans; Acute Coronary Syndrome; Electrocardiography; Myocardial Infarction; Emergency Medical Services; Machine Learning
PubMed: 37665551
DOI: 10.1007/s43678-023-00572-5 -
Scientific Reports Jul 2022Computed tomography (CT) has been widely used to diagnose Graves' orbitopathy, and the utility is gradually increasing. To develop a neural network (NN)-based method for...
Computed tomography (CT) has been widely used to diagnose Graves' orbitopathy, and the utility is gradually increasing. To develop a neural network (NN)-based method for diagnosis and severity assessment of Graves' orbitopathy (GO) using orbital CT, a specific type of NN optimized for diagnosing GO was developed and trained using 288 orbital CT scans obtained from patients with mild and moderate-to-severe GO and normal controls. The developed NN was compared with three conventional NNs [GoogleNet Inception v1 (GoogLeNet), 50-layer Deep Residual Learning (ResNet-50), and 16-layer Very Deep Convolutional Network from Visual Geometry group (VGG-16)]. The diagnostic performance was also compared with that of three oculoplastic specialists. The developed NN had an area under receiver operating curve (AUC) of 0.979 for diagnosing patients with moderate-to-severe GO. Receiver operating curve (ROC) analysis yielded AUCs of 0.827 for GoogLeNet, 0.611 for ResNet-50, 0.540 for VGG-16, and 0.975 for the oculoplastic specialists for diagnosing moderate-to-severe GO. For the diagnosis of mild GO, the developed NN yielded an AUC of 0.895, which is better than the performances of the other NNs and oculoplastic specialists. This study may contribute to NN-based interpretation of orbital CTs for diagnosing various orbital diseases.
Topics: Graves Ophthalmopathy; Humans; Neural Networks, Computer; Tomography, X-Ray Computed
PubMed: 35840769
DOI: 10.1038/s41598-022-16217-z -
Journal of Cardiology Jul 2023Dyspnea is a common symptom in acute heart failure (AHF) patients. Although an accurate and rapid diagnosis of AHF is essential to improve prognosis, estimation of left...
BACKGROUND
Dyspnea is a common symptom in acute heart failure (AHF) patients. Although an accurate and rapid diagnosis of AHF is essential to improve prognosis, estimation of left ventricular (LV) filling pressure (FP) remains challenging, especially for noncardiologists. We evaluated the usefulness of a recently-proposed parameter of LV FP, visually assessed time difference between the mitral valve and tricuspid valve opening (VMT) score, to detect AHF in patients complaining of dyspnea.
METHODS
Echocardiography and lung ultrasonography (LUS) were performed in 121 consecutive patients (68 ± 14 years old, 75 males) presenting with dyspnea. The VMT score was determined from the atrioventricular valve opening phase (tricuspid valve first: 0, simultaneous: 1, mitral valve first: 2) and inferior vena cava dilatation (absent: 0, present: 1), and VMT ≥2 was judged as positive. LUS was performed with the 8 zones method and judged as positive if 3 or more B-lines were observed in bilateral regions. The AHF diagnosis was performed by certified cardiologists according to recent guidelines.
RESULTS
Of the 121 patients, 33 were diagnosed with AHF. The sensitivity and specificity for diagnosing AHF were 64 % and 84 % for LUS and 94 % and 88 % for VMT score. In logistic regression analysis, VMT score showed a significantly higher c-index than LUS (0.91 vs 0.74, p = 0.002). In multivariable analyses, VMT score was associated with AHF independently of clinically relevant covariates and LUS. In addition, serial assessment of VMT score followed by LUS provided a diagnostic flow chart to diagnose AHF (VMT 3: AHF definitive, VMT 2 and LUS positive: AHF highly suspicious; VMT 2 and LUS negative: further investigation is needed; VMT ≤ 1: AHF rejected).
CONCLUSIONS
VMT score showed high diagnostic accuracy in diagnosing AHF. Combined assessment of the VMT score and LUS could become a reliable strategy for diagnosis of AHF by non-cardiologists.
Topics: Male; Humans; Middle Aged; Aged; Aged, 80 and over; Lung; Echocardiography; Ultrasonography; Dyspnea; Heart Failure
PubMed: 37119933
DOI: 10.1016/j.jjcc.2023.04.016