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Clinical Endoscopy Jul 2019Subepithelial tumors are divided into benign subepithelial and potentially malignant gastrointestinal stromal tumors. It is difficult to distinguish between these tumor...
Subepithelial tumors are divided into benign subepithelial and potentially malignant gastrointestinal stromal tumors. It is difficult to distinguish between these tumor types. Contrast-enhanced harmonic endoscopic ultrasound is reportedly useful for diagnosing subepithelial tumors, can be safely and easily performed by understanding the principle and method, and can be used to distinguish between tumor types with high sensitivity on the basis of differences in contrast effect. The generated image shows a hyperenhancement pattern in gastrointestinal stromal tumors (sensitivity, 78%-100%; specificity, 60%-100%; accuracy, 60%-100%) and hypoenhancement pattern in benign subepithelial tumors. Contrast-enhanced harmonic endoscopic ultrasound can be used to estimate the malignancy potential of gastrointestinal stromal tumors by evaluating the uniformity of the contrast and the blood vessels inside the tumor, with abnormal intra-tumor blood vessels, heterogeneous enhancement, and non-enhancing spots suggesting malignancy. Contrast-enhanced harmonic endoscopic ultrasound has a higher sensitivity than other imaging modalities for the detection of vascularity within gastrointestinal stromal tumors. Additionally, it has been reported that treatment effects can be estimated by evaluating the blood flow in the gastrointestinal stromal tumor before and after treatment with tyrosine kinase inhibitors using contrastenhanced ultrasound. However, there will be subjective-bias and the results depends on the performer's skill.
PubMed: 31331161
DOI: 10.5946/ce.2019.056 -
BMC Primary Care Jan 2024In the adult population, about 50% have hypertension, a risk factor for cardiovascular disease and subsequent premature death. Little is known about the quality of the...
BACKGROUND
In the adult population, about 50% have hypertension, a risk factor for cardiovascular disease and subsequent premature death. Little is known about the quality of the methods used to diagnose hypertension in primary care.
OBJECTIVES
The objective was to assess the frequency of use of recognized methods to establish a diagnosis of hypertension, and specifically for OBPM, whether three distinct measurements were taken, and how correctly the blood pressure levels were interpreted.
METHODS
A retrospective population-based cohort study using electronic medical records of patients aged between 40 and 70 years, who visited their general practitioner (GP) with a new-onset of hypertension in the years 2012, 2016, 2019, and 2020. A visual chart review of the electronic medical records was used to assess the methods employed to diagnose hypertension in a random sample of 500 patients. The blood pressure measurement method was considered complete if three or more valid office blood pressure measurements (OBPM) were performed, or home-based blood pressure measurements (HBPM), the office- based 30-minute method (OBP30), or 24-hour ambulatory blood pressure measurements (24 H-ABPM) were used.
RESULTS
In all study years, OBPM was the most frequently used method to diagnose new-onset hypertension in patients. The OBP-30 method was used in 0.4% (2012), 4.2% (2016), 10.6% (2019), and 9.8% (2020) of patients respectively, 24 H-ABPM in 16.0%, 22.2%, 17.2%, and 19.0% of patients and HBPM measurements in 5.4%, 8.4%, 7.6%, and 7.8% of patients, respectively. A diagnosis of hypertension based on only one or two office measurements occurred in 85.2% (2012), 87.9% (2016), 94.4% (2019), and 96.8% (2020) of all patients with OBPM. In cases of incomplete measurement and incorrect interpretation, medication was still started in 64% of cases in 2012, 56% (2016), 60% (2019), and 73% (2020).
CONCLUSION
OBPM is still the most often used method to diagnose hypertension in primary care. The diagnosis was often incomplete or misinterpreted using incorrect cut-off levels. A small improvement occurred between 2012 and 2016 but no further progress was seen in 2019 or 2020. If hypertension is inappropriately diagnosed, it may result in under treatment or in prolonged, unnecessary treatment of patients. There is room for improvement in the general practice setting.
Topics: Adult; Humans; Middle Aged; Aged; Blood Pressure; Blood Pressure Monitoring, Ambulatory; Retrospective Studies; Cohort Studies; Hypertension; Primary Health Care
PubMed: 38166561
DOI: 10.1186/s12875-023-02241-z -
Korean Journal of Radiology Apr 2021The aim of this pilot study was to investigate the potential of early-phase single-photon emission computed tomography (SPECT)/computed tomography (CT) using...
OBJECTIVE
The aim of this pilot study was to investigate the potential of early-phase single-photon emission computed tomography (SPECT)/computed tomography (CT) using technetium-99m methyl diphosphonate (Tc-MDP) for diagnosing osteomyelitis (OM).
MATERIALS AND METHODS
Twenty-one patients with suspected OM were enrolled retrospectively. Three-phase bone scan (TPBS), early-phase SPECT/CT (immediately after blood pool planar imaging), and delayed-phase SPECT/CT (immediately after delayed planar imaging) were performed. The final diagnoses were established through surgery or clinical follow-up for over 6 months. We compared three diagnostic criteria based on (I) TPBS alone, (II) combined TPBS and delayed-phase SPECT/CT, and (III) early-phase SPECT/CT alone.
RESULTS
OM was diagnosed in 11 of 21 patients (nine surgically and two clinically). Of the 11 OM patients, criterion-I, criterion-II, and criterion-III were positive in six, seven, and 10 patients, respectively. Of the 10 non-OM patients, criterion-I, criterion-II, and criterion-III were negative in five, five, and seven patients, respectively. The sensitivity/specificity/accuracy of criterion-I, criterion-II, and criterion-III for diagnosing OM were 54.5%/50.0%/55.0%, 63.6%/50.0%/57.1%, and 90.9%/70.0%/87.5%, respectively.
CONCLUSION
This pilot study demonstrated the potential of using the early-phase SPECT/CT to diagnose OM. Based on the results, prospective studies with a larger sample size should be conducted to confirm the efficacy of early-phase SPECT/CT.
Topics: Adult; Aged; Female; Humans; Image Processing, Computer-Assisted; Male; Middle Aged; Osteomyelitis; Pilot Projects; Retrospective Studies; Sensitivity and Specificity; Skull; Tibia; Tomography, Emission-Computed, Single-Photon; Tomography, X-Ray Computed
PubMed: 33289359
DOI: 10.3348/kjr.2019.0746 -
Clinica Chimica Acta; International... Dec 2022To establish reference intervals (RIs) for PTX-3 and to validate the performance of these RIs in a population including healthy volunteers and Takayasu's arteritis (TAK)...
BACKGROUND AND AIMS
To establish reference intervals (RIs) for PTX-3 and to validate the performance of these RIs in a population including healthy volunteers and Takayasu's arteritis (TAK) patients.
MATERIALS AND METHODS
Plasma PTX-3 levels were determined in 166 healthy volunteers and 63 TAK patients. RIs were established in healthy volunteers according to guidelines from the Clinical and Laboratory Standards Institute (CLSI, C28-A3). Global assessment was used to quantitatively diagnose active/non-active TAK patients. Screening and monitoring performances were validated by identifying active TAK patients from the whole population or diagnosed TAK patients.
RESULTS
The PTX-3 RI was calculated to be 0.87-2.78 ng/mL. For screening purposes, 1.55 ng/mL had a high sensitivity of 90.32 % and the RI upper limit (2.78 ng/mL) had a high specificity of 97.94 %. For monitoring purposes, the sensitivity/specificity of the cut-off value (1.55 ng/mL) and RI median were 90.32 %/90.63 % and 80.85 %/90.63 %, respectively. These screening and monitoring performances of PTX-3 were superior to those of C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR).
CONCLUSION
The distribution of serum PTX-3 levels was stable and uniform across the population. The screening and monitoring performances of the cut-off value and RI-derived values of PTX-3 were higher than CRP and ESR.
Topics: Humans; Takayasu Arteritis; C-Reactive Protein; Healthy Volunteers; Blood Sedimentation; Reference Values
PubMed: 36279941
DOI: 10.1016/j.cca.2022.09.028 -
Environmental Health Perspectives Jul 2002Diagnosing an environmental or occupationally related pulmonary disorder often involves a process of elimination. Unlike commonly diagnosed conditions in other...
Diagnosing an environmental or occupationally related pulmonary disorder often involves a process of elimination. Unlike commonly diagnosed conditions in other specialties, a cause-and-effect relationship may be implied, yet other factors such as temporality and biologic plausibility are lacking. Our patient was referred with a suspected work-related pulmonary disorder. For several years, she had suffered with dyspnea on exertion and repeated flulike illnesses. She worked at an automobile repair garage that performed a large number of emission tests, and there was concern that her workplace exposures were the cause of her symptoms. After a careful review of her history, physical examination, and laboratory testing, we came to the conclusion that she had hypersensitivity pneumonitis related to pet cockatiels in her home. Clinical points of emphasis include the importance of a complete environmental history and careful auscultation of the chest when performing the physical examination. In addition, we encountered an interesting physical diagnostic clue, a respiratory sound that assisted with the eventual diagnosis.
Topics: Animals; Animals, Domestic; Bird Fancier's Lung; Diagnosis, Differential; Female; Humans; Middle Aged; Physical Examination; Psittaciformes; Respiratory Sounds
PubMed: 12117652
DOI: 10.1289/ehp.02110735 -
World Journal of Gastroenterology Jul 2013To examine performances regarding prediction of polyp histology using high-definition (HD) i-scan in a group of endoscopists with varying levels of experience.
AIM
To examine performances regarding prediction of polyp histology using high-definition (HD) i-scan in a group of endoscopists with varying levels of experience.
METHODS
We used a digital library of HD i-scan still images, comprising twin pictures (surface enhancement and tone enhancement), collected at our university hospital. We defined endoscopic features of adenomatous and non-adenomatous polyps, according to the following parameters: color, surface pattern and vascular pattern. We familiarized the participating endoscopists on optical diagnosis of colorectal polyps using a 20-min didactic training session. All endoscopists were asked to evaluate an image set of 50 colorectal polyps with regard to polyp histology. We classified the diagnoses into high confidence (i.e., cases in which the endoscopist could assign a diagnosis with certainty) and low confidence diagnoses (i.e., cases in which the endoscopist preferred to send the polyp for formal histology). Mean sensitivity, specificity and accuracy per endoscopist/image were computed and differences between groups tested using independent-samples t tests. High vs low confidence diagnoses were compared using the paired-samples t test.
RESULTS
Eleven endoscopists without previous experience on optical diagnosis evaluated a total of 550 images (396 adenomatous, 154 non-adenomatous). Mean sensitivity, specificity and accuracy for diagnosing adenomas were 79.3%, 85.7% and 81.1%, respectively. No significant differences were found between gastroenterologists and trainees regarding performances of optical diagnosis (mean accuracy 78.0% vs 82.9%, P = 0.098). Diminutive lesions were predicted with a lower mean accuracy as compared to non-diminutive lesions (74.2% vs 93.1%, P = 0.008). A total of 446 (81.1%) diagnoses were made with high confidence. High confidence diagnoses corresponded to a significantly higher mean accuracy than low confidence diagnoses (84.0% vs 64.3%, P = 0.008). A total of 319 (58.0%) images were evaluated as having excellent quality. Considering excellent quality images in conjunction with high confidence diagnosis, overall accuracy increased to 92.8%.
CONCLUSION
After a single training session, endoscopists with varying levels of experience can already provide optical diagnosis with an accuracy of 84.0%.
Topics: Adenomatous Polyps; Colonic Neoplasms; Colonic Polyps; Colonoscopy; Endoscopes; Gastroenterology; Humans; Observer Variation; Pattern Recognition, Visual; Pilot Projects; Predictive Value of Tests; Prospective Studies; Reproducibility of Results; Time Factors
PubMed: 23885144
DOI: 10.3748/wjg.v19.i27.4334 -
BMC Pediatrics Mar 2024Neonatal respiratory distress syndrome (NRDS) is a prevalent cause of respiratory failure and death among newborns, and prompt diagnosis is imperative. Historically,...
BACKGROUND
Neonatal respiratory distress syndrome (NRDS) is a prevalent cause of respiratory failure and death among newborns, and prompt diagnosis is imperative. Historically, diagnosis of NRDS relied mostly on typical clinical manifestations, chest X-rays, and CT scans. However, recently, ultrasound has emerged as a valuable and preferred tool for aiding NRDS diagnosis. Nevertheless, evaluating lung ultrasound imagery necessitates rigorous training and may be subject to operator-dependent bias, limiting its widespread use. As a result, it is essential to investigate a new, reliable, and operator-independent diagnostic approach that does not require subjective factors or operator expertise. This article aims to explore the diagnostic potential of ultrasound-based radiomics in differentiating NRDS from other non-NRDS lung disease.
METHODS
A total of 150 neonatal lung disease cases were consecutively collected from the department of neonatal intensive care unit of the Quanzhou Maternity and Children's Hospital, Fujian Province, from September 2021 to October 2022. Of these patients, 60 were diagnosed with NRDS, whereas 30 were diagnosed with neonatal pneumonia, meconium aspiration syndrome (MAS), and transient tachypnea (TTN). Two ultrasound images with characteristic manifestations of each lung disease were acquired and divided into training (n = 120) and validation cohorts (n = 30) based on the examination date using an 8:2 ratio. The imaging texture features were extracted using PyRadiomics and, after the screening, machine learning models such as random forest (RF), logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and multilayer perceptron (MLP) were developed to construct an imaging-based diagnostic model. The diagnostic efficacy of each model was analyzed. Lastly, we randomly selected 282 lung ultrasound images and evaluated the diagnostic efficacy disparities between the optimal model and doctors across differing levels of expertise.
RESULTS
Twenty-two imaging-based features with the highest weights were selected to construct a predictive model for neonatal respiratory distress syndrome. All models exhibited favorable diagnostic performances. Analysis of the Youden index demonstrated that the RF model had the highest score in both the training (0.99) and validation (0.90) cohorts. Additionally, the calibration curve indicated that the RF model had the best calibration (P = 0.98). When compared to the diagnostic performance of experienced and junior physicians, the RF model had an area under the curve (AUC) of 0.99; however, the values for experienced and junior physicians were 0.98 and 0.85, respectively. The difference in diagnostic efficacy between the RF model and experienced physicians was not statistically significant (P = 0.24), whereas that between the RF model and junior physicians was statistically significant (P < 0.0001).
CONCLUSION
The RF model exhibited excellent diagnostic performance in the analysis of texture features based on ultrasound radiomics for diagnosing NRDS.
Topics: Humans; Infant, Newborn; Area Under Curve; Meconium Aspiration Syndrome; Radiomics; Respiratory Distress Syndrome, Newborn; Ultrasonography
PubMed: 38528506
DOI: 10.1186/s12887-024-04704-3 -
Radiology Jun 2020Background Although artificial intelligence (AI) shows promise across many aspects of radiology, the use of AI to create differential diagnoses for rare and common...
Background Although artificial intelligence (AI) shows promise across many aspects of radiology, the use of AI to create differential diagnoses for rare and common diseases at brain MRI has not been demonstrated. Purpose To evaluate an AI system for generation of differential diagnoses at brain MRI compared with radiologists. Materials and Methods This retrospective study tested performance of an AI system for probabilistic diagnosis in patients with 19 common and rare diagnoses at brain MRI acquired between January 2008 and January 2018. The AI system combines data-driven and domain-expertise methodologies, including deep learning and Bayesian networks. First, lesions were detected by using deep learning. Then, 18 quantitative imaging features were extracted by using atlas-based coregistration and segmentation. Third, these image features were combined with five clinical features by using Bayesian inference to develop probability-ranked differential diagnoses. Quantitative feature extraction algorithms and conditional probabilities were fine-tuned on a training set of 86 patients (mean age, 49 years ± 16 [standard deviation]; 53 women). Accuracy was compared with radiology residents, general radiologists, neuroradiology fellows, and academic neuroradiologists by using accuracy of top one, top two, and top three differential diagnoses in 92 independent test set patients (mean age, 47 years ± 18; 52 women). Results For accuracy of top three differential diagnoses, the AI system (91% correct) performed similarly to academic neuroradiologists (86% correct; = .20), and better than radiology residents (56%; < .001), general radiologists (57%; < .001), and neuroradiology fellows (77%; = .003). The performance of the AI system was not affected by disease prevalence (93% accuracy for common vs 85% for rare diseases; = .26). Radiologists were more accurate at diagnosing common versus rare diagnoses (78% vs 47% across all radiologists; < .001). Conclusion An artificial intelligence system for brain MRI approached overall top one, top two, and top three differential diagnoses accuracy of neuroradiologists and exceeded that of less-specialized radiologists. © RSNA, 2020 See also the editorial by Zaharchuk in this issue.
Topics: Adult; Aged; Artificial Intelligence; Brain; Brain Diseases; Diagnosis, Computer-Assisted; Diagnosis, Differential; Female; Humans; Magnetic Resonance Imaging; Male; Middle Aged; Rare Diseases; Retrospective Studies; Sensitivity and Specificity
PubMed: 32255417
DOI: 10.1148/radiol.2020190283 -
BMC Medicine May 2022Congenital long QT syndrome (LQTS) is a rare heart disease caused by various underlying mutations. Most general cardiologists do not routinely see patients with...
BACKGROUND
Congenital long QT syndrome (LQTS) is a rare heart disease caused by various underlying mutations. Most general cardiologists do not routinely see patients with congenital LQTS and may not always recognize the accompanying ECG features. In addition, a proportion of disease carriers do not display obvious abnormalities on their ECG. Combined, this can cause underdiagnosing of this potentially life-threatening disease.
METHODS
This study presents 1D convolutional neural network models trained to identify genotype positive LQTS patients from electrocardiogram as input. The deep learning (DL) models were trained with a large 10-s 12-lead ECGs dataset provided by Amsterdam UMC and externally validated with a dataset provided by University Hospital Leuven. The Amsterdam dataset included ECGs from 10000 controls, 172 LQTS1, 214 LQTS2, and 72 LQTS3 patients. The Leuven dataset included ECGs from 2200 controls, 32 LQTS1, and 80 LQTS2 patients. The performance of the DL models was compared with conventional QTc measurement and with that of an international expert in congenital LQTS (A.A.M.W). Lastly, an explainable artificial intelligence (AI) technique was used to better understand the prediction models.
RESULTS
Overall, the best performing DL models, across 5-fold cross-validation, achieved on average a sensitivity of 84 ± 2%, 90 ± 2% and 87 ± 6%, specificity of 96 ± 2%, 95 ± 1%, and 92 ± 4%, and AUC of 0.90 ± 0.01, 0.92 ± 0.02, and 0.89 ± 0.03, for LQTS 1, 2, and 3 respectively. The DL models were also shown to perform better than conventional QTc measurements in detecting LQTS patients. Furthermore, the performances held up when the DL models were validated on a novel external cohort and outperformed the expert cardiologist in terms of specificity, while in terms of sensitivity, the DL models and the expert cardiologist in LQTS performed the same. Finally, the explainable AI technique identified the onset of the QRS complex as the most informative region to classify LQTS from non-LQTS patients, a feature previously not associated with this disease.
CONCLUSIONS
This study suggests that DL models can potentially be used to aid cardiologists in diagnosing LQTS. Furthermore, explainable DL models can be used to possibly identify new features for LQTS on the ECG, thus increasing our understanding of this syndrome.
Topics: Artificial Intelligence; Deep Learning; Electrocardiography; Humans; Long QT Syndrome; Neural Networks, Computer
PubMed: 35501785
DOI: 10.1186/s12916-022-02350-z -
Medical Ultrasonography Aug 2018Renal artery duplex ultrasonography (RDU) is an effective and non-invasive screening test in diagnosing renal artery stenosis. The discordance of results in multiple...
AIM
Renal artery duplex ultrasonography (RDU) is an effective and non-invasive screening test in diagnosing renal artery stenosis. The discordance of results in multiple RDU is common. We aim to evaluate the discordance and the reasons for discordance between diagnoses and measurements from multiple RDU examinations.
MATERIAL AND METHOD
A retrospective study was performed in 64 examinations of renal arteries from 32 patients that were referred for two or more RDU examinations and renal artery digital subtraction angiography (DSA) within six months, between August 2013 and January 2016. Using DSA as gold standard, we divided the renal arteries into three groups: discordant (one diagnosis of RDU was correct and one was wrong), misdiagnosed (neither RDU diagnosis was correct) and correct (both RDU diagnoses were correct) groups.We evaluated the discordance and reasons for discordance of diagnoses and measurements from multiple RDU examinations. Results: Among 64 renal arteries included in this study, 37 renal arteries had two correct diagnoses, 19 renal arteries had two discordant diagnoses, and eight renal arteries were misdiagnosed twice by RDU. The discordance of peak systolic velocity (PSV), the ratio between PSV in the renal artery with stenosis and PSV in the aorta (RAR), and tardus-parvus waveform measurements were clearly higher in the discordant diagnoses group than in the correctly diagnosed group. The most common reason for a discordant diagnosis was failure in obtaining correct tardus-parvus waveforms of the interlobar artery (26.31%). Themost common reason for misdiagnosis was the presence of an extremely severe stenosis with an atrophic kidney (31.25%). Overall, 87.50% of patients underwent RDU examinations had correct diagnoses of stenosis or occlusion at least once (including location and degree), as confirmed by DSA.
CONCLUSIONS
Our study indicates that standard operating procedures and improvements in examination technique by ultrasound doctors could reduce the discordance between multiple tests.
Topics: Adult; Aged; Cohort Studies; Diagnostic Errors; Female; Hospitals, University; Humans; Male; Middle Aged; Renal Artery; Renal Artery Obstruction; Retrospective Studies; Sensitivity and Specificity; Severity of Illness Index; Ultrasonography, Doppler, Duplex
PubMed: 30167582
DOI: 10.11152/mu-1341