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Preferred diagnostic methods of pyriform sinus fistula in different situations: A systematic review.American Journal of Otolaryngology 2023Pyriform sinus fistula (PSF) diagnosis is often easily delayed and incorrect. Diagnostic values of modalities vary in different situations. The aim of this study was to... (Review)
Review
PURPOSE
Pyriform sinus fistula (PSF) diagnosis is often easily delayed and incorrect. Diagnostic values of modalities vary in different situations. The aim of this study was to recommend optimal schemes for diagnosing PSF at different ages and infection stages.
METHODS
A search of PubMed, Embase, Cochrane Library, and CBM databases was conducted to identify articles written in Chinese and English concerning PSF diagnosis using keywords: "pyriform sinus fistula", "diagnosis", and relevant synonymous terms. Quality assessment was performed using the Joanna Briggs Institute (JBI) levels of evidence and critical appraisal checklist tool.
RESULTS
111 studies describing 3692 patients were included. The highest true positive rate (TPR) of ultrasonography was 66.67 % in adult cases. Computed tomography (CT) yielded a good TPR (approximately 73 %) in both neonatal and adult patients, and contrast-enhanced CT (84.21 %) was better in adult patients. Most children cases could be accurately diagnosed by barium swallow (BS) examination which was significantly different in acute and non-infection stages (AIS, NIS). Magnetic resonance imaging (MRI) produced a nice TPR in fetal cases (69.23 %) and neonatal cases (54.44 %). Laryngoscopy was also affected by infection stages. TPR of gastroscopy (GS) was the highest in children (86.36 %) and adult cases (87.50 %).
CONCLUSION
For fetal cases suspected of PSF, an MRI is recommended. MRI or CT is preferred for neonatal cases regardless of infection stages. Children and adult patients are advised to undergo GS during NIS or AIS, while BS is suggested for NIS. Contrast-enhanced CT can also diagnose adults with PSF in AIS.
Topics: Child; Infant, Newborn; Humans; Pyriform Sinus; Tomography, X-Ray Computed; Ultrasonography; Laryngoscopy; Fistula; Retrospective Studies
PubMed: 36584597
DOI: 10.1016/j.amjoto.2022.103747 -
Annals of Nuclear Medicine Mar 2024Rheumatoid Arthritis (RA) is a systemic inflammatory disorder that commonly presents with polyarthritis but can have multisystemic involvement and complications, leading... (Review)
Review
Rheumatoid Arthritis (RA) is a systemic inflammatory disorder that commonly presents with polyarthritis but can have multisystemic involvement and complications, leading to increased morbidity and mortality. The diagnosis of RA continues to be challenging due to its varied clinical presentations. In this review article, we aim to determine the potential of PET/CT to assist in the diagnosis of RA and its complications, evaluate the therapeutic response to treatment, and predict RA remission. PET/CT has increasingly been used in the last decade to diagnose, monitor treatment response, predict remissions, and diagnose subclinical complications in RA. PET imaging with [F]-fluorodeoxyglucose ([F]-FDG) is the most commonly applied radiotracer in RA, but other tracers are also being studied. PET/CT with [F]-FDG, [F]-NaF, and other tracers might lead to early identification of RA and timely evidence-based clinical management, decreasing morbidity and mortality. Although PET/CT has been evolving as a promising tool for evaluating and managing RA, more evidence is required before incorporating PET/CT in the standard clinical management of RA.
Topics: Humans; Positron Emission Tomography Computed Tomography; Fluorodeoxyglucose F18; Arthritis, Rheumatoid; Positron-Emission Tomography; Radiopharmaceuticals
PubMed: 38277115
DOI: 10.1007/s12149-023-01896-z -
European Radiology Dec 2023PSC strictures are routinely diagnosed on T2-MRCP as dominant- (DS) or high-grade stricture (HGS). However, high inter-observer variability limits their utility. We...
OBJECTIVES
PSC strictures are routinely diagnosed on T2-MRCP as dominant- (DS) or high-grade stricture (HGS). However, high inter-observer variability limits their utility. We introduce the "potential functional stricture" (PFS) on T1-weighted hepatobiliary-phase images of gadoxetic acid-enhanced MR cholangiography (T1-MRC) to assess inter-reader agreement on diagnosis, location, and prognostic value of PFS on T1-MRC vs. DS or HGS on T2-MRCP in PSC patients, using ERCP as the gold standard.
METHODS
Six blinded readers independently reviewed 129 MRIs to diagnose and locate stricture, if present. DS/HGS was determined on T2-MRCP. On T1-MRC, PFS was diagnosed if no GA excretion was seen in the CBD, hilum or distal RHD, or LHD. If excretion was normal, "no functional stricture" (NFS) was diagnosed. T1-MRC diagnoses (NFS = 87; PFS = 42) were correlated with ERCP, clinical scores, labs, splenic volume, and clinical events. Statistical analyses included Kaplan-Meier curves and Cox regression.
RESULTS
Interobserver agreement was almost perfect for NFS vs. PFS diagnosis, but fair to moderate for DS and HGS. Forty-four ERCPs in 129 patients (34.1%) were performed, 39 in PFS (92.9%), and, due to clinical suspicion, five in NFS (5.7%) patients. PFS and NFS diagnoses had 100% PPV and 100% NPV, respectively. Labs and clinical scores were significantly worse for PFS vs. NFS. PFS patients underwent more diagnostic and therapeutic ERCPs, experienced more clinical events, and reached significantly more endpoints (p < 0.001) than those with NFS. Multivariate analysis identified PFS as an independent risk factor for liver-related events.
CONCLUSION
T1-MRC was superior to T2-MRCP for stricture diagnosis, stricture location, and prognostication.
CLINICAL RELEVANCE STATEMENT
Because half of PSC patients will develop clinically-relevant strictures over the course of the disease, earlier more confident diagnosis and correct localization of functional stricture on gadoxetic acid-enhanced MRI may optimize management and improve prognostication.
KEY POINTS
• There is no consensus regarding biliary stricture imaging features in PSC that have clinical relevance. • Twenty-minute T1-weighted MRC images correctly classified PSC patients with potential (PFS) vs with no functional stricture (NFS). • T1-MRC diagnoses may reduce the burden of diagnostic ERCPs.
Topics: Humans; Cholangiopancreatography, Magnetic Resonance; Constriction, Pathologic; Cholangitis, Sclerosing; Retrospective Studies; Magnetic Resonance Imaging; Cholangiopancreatography, Endoscopic Retrograde
PubMed: 37470827
DOI: 10.1007/s00330-023-09915-3 -
BMC Oral Health Jun 2023Artificial intelligence (AI) has been introduced to interpret the panoramic radiographs (PRs). The aim of this study was to develop an AI framework to diagnose multiple...
BACKGROUND
Artificial intelligence (AI) has been introduced to interpret the panoramic radiographs (PRs). The aim of this study was to develop an AI framework to diagnose multiple dental diseases on PRs, and to initially evaluate its performance.
METHODS
The AI framework was developed based on 2 deep convolutional neural networks (CNNs), BDU-Net and nnU-Net. 1996 PRs were used for training. Diagnostic evaluation was performed on a separate evaluation dataset including 282 PRs. Sensitivity, specificity, Youden's index, the area under the curve (AUC), and diagnostic time were calculated. Dentists with 3 different levels of seniority (H: high, M: medium, L: low) diagnosed the same evaluation dataset independently. Mann-Whitney U test and Delong test were conducted for statistical analysis (ɑ=0.05).
RESULTS
Sensitivity, specificity, and Youden's index of the framework for diagnosing 5 diseases were 0.964, 0.996, 0.960 (impacted teeth), 0.953, 0.998, 0.951 (full crowns), 0.871, 0.999, 0.870 (residual roots), 0.885, 0.994, 0.879 (missing teeth), and 0.554, 0.990, 0.544 (caries), respectively. AUC of the framework for the diseases were 0.980 (95%CI: 0.976-0.983, impacted teeth), 0.975 (95%CI: 0.972-0.978, full crowns), and 0.935 (95%CI: 0.929-0.940, residual roots), 0.939 (95%CI: 0.934-0.944, missing teeth), and 0.772 (95%CI: 0.764-0.781, caries), respectively. AUC of the AI framework was comparable to that of all dentists in diagnosing residual roots (p > 0.05), and its AUC values were similar to (p > 0.05) or better than (p < 0.05) that of M-level dentists for diagnosing 5 diseases. But AUC of the framework was statistically lower than some of H-level dentists for diagnosing impacted teeth, missing teeth, and caries (p < 0.05). The mean diagnostic time of the framework was significantly shorter than that of all dentists (p < 0.001).
CONCLUSIONS
The AI framework based on BDU-Net and nnU-Net demonstrated high specificity on diagnosing impacted teeth, full crowns, missing teeth, residual roots, and caries with high efficiency. The clinical feasibility of AI framework was preliminary verified since its performance was similar to or even better than the dentists with 3-10 years of experience. However, the AI framework for caries diagnosis should be improved.
Topics: Humans; Radiography, Panoramic; Artificial Intelligence; Tooth, Impacted; Dental Caries; Tooth
PubMed: 37270488
DOI: 10.1186/s12903-023-03027-6 -
The Journal of Allergy and Clinical... Nov 2022Primary immunodeficiency diseases (PIDD) are a group of immune-related disorders that have a current median delay of diagnosis between 6 and 9 years. Early diagnosis and...
BACKGROUND
Primary immunodeficiency diseases (PIDD) are a group of immune-related disorders that have a current median delay of diagnosis between 6 and 9 years. Early diagnosis and treatment of PIDD has been associated with improved patient outcomes.
OBJECTIVE
To develop a machine learning model using elements within the electronic health record data that are related to prior symptomatic treatment to predict PIDD.
METHODS
We conducted a retrospective study of patients with PIDD identified using inclusion criteria of PIDD-related diagnoses, immunodeficiency-specific medications, and low immunoglobulin levels. We constructed a control group of age-, sex-, and race-matched patients with asthma. The primary outcome was the diagnosis of PIDD. We considered comorbidities, laboratory tests, medications, and radiological orders as features, all before diagnosis and indicative of symptom-related treatment. Features were presented sequentially to logistic regression, elastic net, and random forest classifiers, which were trained using a nested cross-validation approach.
RESULTS
Our cohort consisted of 6422 patients, of whom 247 (4%) were diagnosed with PIDD. Our logistic regression model with comorbidities demonstrated good discrimination between patients with PIDD and those with asthma (c-statistic: 0.62 [0.58-0.65]). Adding laboratory results, medications, and radiological orders improved discrimination (c-statistic: 0.70 vs 0.62, P < .001), sensitivity, and specificity. Extending to the advanced machine learning models did not improve performance.
CONCLUSIONS
We developed a prediction model for early diagnosis of PIDD using historical data that are related to symptomatic care, which has potential to fill an important need in reducing the time to diagnose PIDD, leading to better outcomes for immunodeficient patients.
Topics: Humans; Retrospective Studies; Immunologic Deficiency Syndromes; Machine Learning; Early Diagnosis; Primary Immunodeficiency Diseases; Asthma
PubMed: 36108921
DOI: 10.1016/j.jaip.2022.08.041 -
BMC Medical Informatics and Decision... Mar 2022Acute Rheumatic Fever (ARF) is a critically important condition for which there is no diagnostic test. Diagnosis requires the use of a set of criteria comprising...
BACKGROUND
Acute Rheumatic Fever (ARF) is a critically important condition for which there is no diagnostic test. Diagnosis requires the use of a set of criteria comprising clinical, laboratory, electrocardiographic and echocardiographic findings. The complexity of the algorithm and the fact that clinicians lack familiarity with ARF, make ARF diagnosis ideally suited to an electronic decision support tool. The ARF Diagnosis Calculator was developed to assist clinicians in diagnosing ARF and correctly assign categories of 'possible, 'probable' or 'definite' ARF. This research aimed to evaluate the acceptability, accuracy, and test performance of the ARF Diagnosis Calculator.
METHODS
Three strategies were used to provide triangulation of data. Users of the calculator employed at Top End Health Service, Northern Territory, Australia were invited to participate in an online survey, and clinicians with ARF expertise were invited to participate in semi-structured interviews. Qualitative data were analysed using inductive analysis. Performance of the calculator in correctly diagnosing ARF was assessed using clinical data from 35 patients presenting with suspected ARF. Diagnoses obtained from the calculator were compared using the Kappa statistic with those obtained from a panel of expert clinicians.
RESULTS
Survey responses were available from 23 Top End Health Service medical practitioners, and interview data were available from five expert clinicians. Using a 6-point Likert scale, participants highly recommended the ARF Diagnosis Calculator (median 6, IQR 1), found it easy to use (median 5, IQR 1) and believed the calculator helped them diagnose ARF (median 5, IQR 1). Clinicians with ARF expertise noted that electronic decision making is not a substitute for clinical experience. There was high agreement between the ARF Diagnosis Calculator and the 'gold standard' ARF diagnostic process (κ = 0.767, 95% CI: 0.568-0.967). Incorrect assignment of diagnosis occurred in 4/35 (11%) patients highlighting the greater accuracy of expert clinical input for ambiguous presentations. Sixteen changes were incorporated into a revised version of the calculator.
CONCLUSIONS
The ARF Diagnosis Calculator is an easy-to-use, accessible tool, but it does not replace clinical expertise. The calculator performed well amongst clinicians and is an acceptable tool for use within the clinical setting with a high level of accuracy in comparison to the gold standard diagnostic process. Effective resources to support clinicians are critically important for improving the quality of care of ARF.
Topics: Echocardiography; Humans; Northern Territory; Rheumatic Fever; Surveys and Questionnaires
PubMed: 35346167
DOI: 10.1186/s12911-022-01816-7 -
Developmental Medicine and Child... Aug 2021To explore the attitudes of paediatric intensive care unit (PICU) health care professionals towards diagnosis and neurophysiological monitoring of seizures.
AIM
To explore the attitudes of paediatric intensive care unit (PICU) health care professionals towards diagnosis and neurophysiological monitoring of seizures.
METHOD
This study used an explanatory sequential mixed-methods approach, interconnecting quantitative and qualitative features, comprising questionnaires and interviews, with equal weighting between stages, of health care professionals working in UK PICUs. Interview data were analysed using thematic analysis and triangulated with questionnaire data.
RESULTS
Seventy-two questionnaires were returned: 49 out of 60 (71.0%) of respondents reported that seizures were extremely hard or somewhat hard to diagnose in a critically ill child, and 81.2% had seen misdiagnosis occur. Thematic analysis revealed two main themes: (1) feeling out of control when faced with 'grey areas'; and (2) regaining control, which compromised three subthemes: aggressive intervention, accurate diagnosis, and eschewing diagnosis.
INTERPRETATION
Health care professionals find accurate diagnosis of seizures difficult, particularly in sedated/paralysed children and those with chronic neurological disorders. They report they would like better educational opportunities on discriminating between epileptic and non-epileptic events to improve their confidence. Professionals want routine neurophysiological monitoring that can be applied and interpreted at the bedside throughout the day to regain a sense of control over their patient, direct treatment appropriately, and, potentially, improve outcomes, but report appropriate training and peer review are essential if it is to be introduced into routine care. What this study adds Paediatric intensive care unit (PICU) staff feel out of control when faced with diagnosing seizures. Neurophysiological monitoring is wanted to help diagnosis and treatment. Amplitude-integrated electroencephalography is the preferred, pragmatic tool by PICU staff.
Topics: Attitude of Health Personnel; Critical Illness; Electroencephalography; Health Knowledge, Attitudes, Practice; Health Personnel; Humans; Intensive Care Units, Pediatric; Neurophysiological Monitoring; Seizures; Surveys and Questionnaires
PubMed: 33913148
DOI: 10.1111/dmcn.14907 -
Journal of Cachexia, Sarcopenia and... Dec 2021Sarcopenia is defined as muscle wasting, characterized by a progressive loss of muscle mass and function due to ageing. Diagnosis of sarcopenia typically involves both...
BACKGROUND
Sarcopenia is defined as muscle wasting, characterized by a progressive loss of muscle mass and function due to ageing. Diagnosis of sarcopenia typically involves both muscle imaging and the physical performance of people exhibiting signs of muscle weakness. Despite its worldwide prevalence, a molecular method for accurately diagnosing sarcopenia has not been established.
METHODS
We develop an artificial intelligence (AI) diagnosis model of sarcopenia using a published transcriptome dataset comprising patients from multiple ethnicities. For the AI model for sarcopenia diagnosis, we use a transcriptome database comprising 17 339 genes from 118 subjects. Among the 17 339 genes, we select 27 features as the model inputs. For feature selection, we use a random forest, extreme gradient boosting and adaptive boosting. Using the top 27 features, we propose a four-layer deep neural network, named DSnet-v1, for sarcopenia diagnosis.
RESULTS
Among isolated testing datasets, DSnet-v1 provides high sensitivity (100%), specificity (94.12%), accuracy (95.83%), balanced accuracy (97.06%) and area under receiver operating characteristics (0.99). To extend the number of patient data, we develop a web application (http://sarcopeniaAI.ml/), where the model can be accessed unrestrictedly to diagnose sarcopenia if the transcriptome is available. A focused analysis of the top 27 genes for their differential or co-expression with other genes implied the potential existence of race-specific factors for sarcopenia, suggesting the possibility of identifying causal factors of sarcopenia when a more extended dataset is provided.
CONCLUSIONS
Our new AI model, DSnet-v1, accurately diagnoses sarcopenia and is currently available publicly to assist healthcare providers in diagnosing and treating sarcopenia.
Topics: Artificial Intelligence; Biomarkers; Humans; Intelligence; Prognosis; Sarcopenia
PubMed: 34704369
DOI: 10.1002/jcsm.12840 -
The Korean Journal of Gastroenterology... Feb 2022The Chicago Classification is being revised continuously for the accurate diagnosis of esophageal peristaltic disorders in which the etiology is unclear, and the disease... (Review)
Review
The Chicago Classification is being revised continuously for the accurate diagnosis of esophageal peristaltic disorders in which the etiology is unclear, and the disease behavior is heterogeneous. The ver. 4.0 was recently updated. A representative change in the diagnosis of esophageal peristaltic disorders of the ver. 4.0 showed that the distinction between major and minor disorders was eliminated and was divided into the following four diagnoses: absent contractility, distal esophageal spasm (DES), hypercontractile esophagus (HE), and ineffective esophageal motility. Compared to the ver. 3.0, it recommended a more detailed protocol of high-resolution esophageal manometry and methods of interpreting manometric. In addition, it emphasized the clinically relevant symptoms in diagnosing DES and HE, and presented provocative tests (e.g., multiple rapid swallow and rapid drinking challenge), as well as additional testing, including impedance, timed barium esophagogram and functional lumen imaging probe, which may provide more standardized and rigorous criteria for peristaltic patterns and to minimize the ambiguity in diagnosis. Although it will take time and effort to apply this revised Chicago Classification in clinical practice, it may help diagnose and manage patients with esophageal peristalsis disorder in the future.
Topics: Esophageal Achalasia; Esophageal Motility Disorders; Humans; Manometry; Peristalsis
PubMed: 35232921
DOI: 10.4166/kjg.2022.016 -
Clinical Microbiology and Infection :... Apr 2020Scents and odours characterize some microbes when grown in the laboratory, and experienced clinicians can diagnose patients with some infectious diseases based on their... (Review)
Review
BACKGROUND
Scents and odours characterize some microbes when grown in the laboratory, and experienced clinicians can diagnose patients with some infectious diseases based on their smell. Animal sniffing is an innate behaviour, and animals' olfactory acuity is used for detecting people, weapons, bombs, narcotics and food.
OBJECTIVES
We briefly summarized current knowledge regarding the use of sniffing animals to diagnose some infectious diseases and the potential use of scent-based diagnostic instruments in microbiology.
SOURCES
Information was sought through PubMed and extracted from peer-reviewed literature published between January 2000 and September 2019 and from reliable online news. The search terms 'odour', 'scent', 'bacteria', 'diagnostics', 'tuberculosis', 'malaria' and 'volatile compounds' were used.
CONTENT
Four major areas of using sniffing animals are summarized. Dogs have been used to reliably detect stool associated with toxigenic Clostridioides difficile and for surveillance. Dogs showed high sensitivity and moderate specificity for detecting urinary tract infections in comparison to culture, especially for Escherichia coli. African giant pouched rats showed superiority for diagnosing tuberculosis over microscopy, but inferiority to culture/molecular methods. Several approaches for detecting malaria by analysing host skin odour or exhaled breath have been explored successfully. Some microbial infections produce specific volatile organic compounds (VOCs), which can be analysed by spectrometry, metabolomics or other analytical approaches to replace animal sniffing.
IMPLICATIONS
The results of sniffing animal studies are fascinating, and animal sniffing can provide intermediate diagnostic solutions for some infectious diseases. Lack of reproducibility, and cost of animal training and housing are major drawbacks for wider implementation of sniffing animals. The ultimate goal is to understand the biological background of this animal ability and to characterize the specific VOCs that animals are recognizing. VOC identification, improvement of odour sampling methods and development of point-of-care instruments could allow implementation of scent-based tests for major human pathogens.
Topics: Animals; Breath Tests; Communicable Diseases; Dogs; Feces; Humans; Malaria; Microbiological Techniques; Odorants; Rats; Sensitivity and Specificity; Smell; Volatile Organic Compounds
PubMed: 31734357
DOI: 10.1016/j.cmi.2019.10.036