-
Best Practice & Research. Clinical... Mar 2023The skin biopsy plays an important and powerful role in helping diagnose rheumatic diseases that present with cutaneous findings. As the skin is a very accessible organ,... (Review)
Review
The skin biopsy plays an important and powerful role in helping diagnose rheumatic diseases that present with cutaneous findings. As the skin is a very accessible organ, and the skin biopsy can be performed quickly as an in-office procedure, the skin biopsy is utilized frequently in patients with rheumatic diseases. However, the more challenging aspects of performing the biopsy, such as identifying the type of biopsy to perform, the site (s) to biopsy, the type of media to use, and the interpretation of histopathologic data are nuanced and require considerable thought. In this review, we discuss the common skin findings in rheumatic diseases and the general indications for skin biopsies in these diseases. We then summarize how to perform various skin biopsy techniques and how to select the biopsy technique. Finally, we discuss important rheumatic disease-specific considerations for skin biopsy, including where to biopsy and how to interpret the pathologic reports.
Topics: Humans; Skin Diseases; Dermatomyositis; Rheumatologists; Rheumatic Diseases; Biopsy; Skin
PubMed: 37268560
DOI: 10.1016/j.berh.2023.101838 -
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 -
Clinical Microbiology and Infection :... Aug 2023Invasive fungal infections are an important cause of morbidity and mortality in a broad range of patients. Adequate and early diagnosis is a challenge and of importance... (Review)
Review
BACKGROUND
Invasive fungal infections are an important cause of morbidity and mortality in a broad range of patients. Adequate and early diagnosis is a challenge and of importance for improved survival. New molecular-based diagnostic methods are trendsetting, yet with the drawback that conventional tests receive less attention, in the laboratory as well as in the clinical setting.
OBJECTIVES
We aimed to provide a useful recommendation for direct microscopy for effectively managing numerous specimens related to fungal infections, mainly covering opportunistic pathogens.
SOURCES
A PubMed literature search covering direct fungal microscopy was performed with no restrictions on publication dates.
CONTENT
Best practise recommendations targeting the role of direct microscopy in diagnosing fungal infections are given. This review highlights when to perform direct microscopy, displays the main fungal morphologies, discusses the pitfalls related to microscopy, and recommends how to best report the results to clinicians.
IMPLICATION
In many samples, the performance of direct microscopy provides an important diagnostic benefit that is greater than culture alone. Fluorescent dyes improve sensitivity and allow a fast and rapid read. Reporting includes the presence or absence of yeast forms, septate or non-septate hyphae, pigmentation, cellular location, or any other specific structures being present. The visualization of fungal elements from a sterile body site is proof of an infection, independent of other test reports.
Topics: Humans; Microscopy; Mycoses; Yeasts
PubMed: 37187349
DOI: 10.1016/j.cmi.2023.05.012 -
Zeitschrift Fur Gastroenterologie Jan 2016Diagnosing diverticulitis implies physical and laboratory examination, cross-sectional imaging (computed tomography [CT] or ultrasonography [US]), and a classification... (Review)
Review
Diagnosing diverticulitis implies physical and laboratory examination, cross-sectional imaging (computed tomography [CT] or ultrasonography [US]), and a classification of the type of diverticular disease. This article illustrates the role of ultrasonography in view of the recently published Guidelines on diverticular disease of the Consensus Conference of the German Societies of Gastroenterology (DGVS) and Visceral Surgery (DGAV). The focus is to foster both sensitivity for pictorial analysis and improving practical accomplishments of US in diverticulitis. Based on the German classification of diverticular disease (CDD), characteristic features of each type of diverticulitis are presented and commented along with possible differential diagnoses. In the literature qualified US is equipotent to qualified CT. US is frequently effective for the diagnosis and unsurpassed resolution enables detailed imaging thereby allowing one to differentiate and stratify the relevant types of diverticular disease according to the new classification. This educational review is a guided tour through the different facettes of diverticulitis on ultrasonography thereby expanding and multiplying individual competence to more users. With expert performance, US is in the pole position for diagnosing diverticulitis, however, this does come with the price of responsibility and requires transfer of advanced standards and performance in the broad.
Topics: Acute Disease; Diagnosis, Differential; Diverticulitis; Evidence-Based Medicine; Humans; Image Enhancement; Patient Positioning; Ultrasonography
PubMed: 26751117
DOI: 10.1055/s-0041-108204 -
Journal of Neurology Sep 2022Numerous sonographic modalities and parameters have been used to diagnose carpal tunnel syndrome (CTS), with varying accuracy. Our umbrella review aimed to summarize the... (Review)
Review
BACKGROUND
Numerous sonographic modalities and parameters have been used to diagnose carpal tunnel syndrome (CTS), with varying accuracy. Our umbrella review aimed to summarize the evidence from systematic reviews and meta-analyses regarding the use of ultrasound imaging to diagnose CTS.
METHODS
Systematic reviews and meta-analyses meeting the inclusion criteria were searched in PubMed, Embase, Medline, Web of Science, and Cochrane databases from inception to March 2022. Critical appraisal, data extraction, and synthesis were performed in accordance with the criteria for conducting an umbrella review.
RESULTS
Sixteen reviews were included. Three reviews were classified as high quality, one as moderate, four as low, and eight as critically low. The cross-sectional area (CSA) of the median nerve at the carpal tunnel inlet demonstrated the best reliability and diagnostic accuracy among multiple parameters. A cutoff CSA value of 9-10.5 mm gave the highest diagnostic performance in the general population. The degree of CSA enlargement was correlated with CTS severity. Sonoelastography and Doppler ultrasound might provide additional insights into CTS evaluation as median nerve stiffness and vascularity at the wrist were increased in these patients.
CONCLUSIONS
Sonography is a reliable tool to diagnose CTS, with inlet CSA being the most robust parameter. Sonoelastography and Doppler ultrasound can serve as auxiliary tools to confirm CTS diagnoses. Further studies are needed to expand the use of sonography for diagnosing CTS, especially in the presence of concomitant neuromuscular disease(s).
Topics: Carpal Tunnel Syndrome; Humans; Median Nerve; Neural Conduction; Reproducibility of Results; Sensitivity and Specificity; Ultrasonography
PubMed: 35639198
DOI: 10.1007/s00415-022-11201-z -
Journal of Medical Internet Research Jul 2021Diabetic retinopathy (DR), whose standard diagnosis is performed by human experts, has high prevalence and requires a more efficient screening method. Although machine... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Diabetic retinopathy (DR), whose standard diagnosis is performed by human experts, has high prevalence and requires a more efficient screening method. Although machine learning (ML)-based automated DR diagnosis has gained attention due to recent approval of IDx-DR, performance of this tool has not been examined systematically, and the best ML technique for use in a real-world setting has not been discussed.
OBJECTIVE
The aim of this study was to systematically examine the overall diagnostic accuracy of ML in diagnosing DR of different categories based on color fundus photographs and to determine the state-of-the-art ML approach.
METHODS
Published studies in PubMed and EMBASE were searched from inception to June 2020. Studies were screened for relevant outcomes, publication types, and data sufficiency, and a total of 60 out of 2128 (2.82%) studies were retrieved after study selection. Extraction of data was performed by 2 authors according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), and the quality assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Meta-analysis of diagnostic accuracy was pooled using a bivariate random effects model. The main outcomes included diagnostic accuracy, sensitivity, and specificity of ML in diagnosing DR based on color fundus photographs, as well as the performances of different major types of ML algorithms.
RESULTS
The primary meta-analysis included 60 color fundus photograph studies (445,175 interpretations). Overall, ML demonstrated high accuracy in diagnosing DR of various categories, with a pooled area under the receiver operating characteristic (AUROC) ranging from 0.97 (95% CI 0.96-0.99) to 0.99 (95% CI 0.98-1.00). The performance of ML in detecting more-than-mild DR was robust (sensitivity 0.95; AUROC 0.97), and by subgroup analyses, we observed that robust performance of ML was not limited to benchmark data sets (sensitivity 0.92; AUROC 0.96) but could be generalized to images collected in clinical practice (sensitivity 0.97; AUROC 0.97). Neural network was the most widely used method, and the subgroup analysis revealed a pooled AUROC of 0.98 (95% CI 0.96-0.99) for studies that used neural networks to diagnose more-than-mild DR.
CONCLUSIONS
This meta-analysis demonstrated high diagnostic accuracy of ML algorithms in detecting DR on color fundus photographs, suggesting that state-of-the-art, ML-based DR screening algorithms are likely ready for clinical applications. However, a significant portion of the earlier published studies had methodology flaws, such as the lack of external validation and presence of spectrum bias. The results of these studies should be interpreted with caution.
Topics: Algorithms; Diabetes Mellitus; Diabetic Retinopathy; Diagnostic Techniques, Ophthalmological; Humans; Machine Learning; Neural Networks, Computer
PubMed: 34407500
DOI: 10.2196/23863 -
Computational and Mathematical Methods... 2022Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good... (Comparative Study)
Comparative Study
Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good possibility of recovery. Mammography is proven to be an excellent screening technique for breast tumor diagnosis, but its detection and classification in mammograms remain a significant challenge. Previous studies' major limitation is an increase in false positive ratio (FPR) and false negative ratio (FNR), as well as a drop in Matthews correlation coefficient (MCC) value. A model that can lower FPR and FNR while increasing MCC value is required. To overcome prior research limitations, a modified network of YOLOv5 is used in this study to detect and classify breast tumors. Our research is conducted using publicly available datasets Curated Breast Imaging Subset of DDSM (CBIS-DDSM). The first step is to perform preprocessing, which includes image enhancing techniques and the removal of pectoral muscles and labels. The dataset is then annotated, augmented, and divided into 60% for training, 30% for validation, and 10% for testing. The experiment is then performed using a batch size of 8, a learning rate of 0.01, a momentum of 0.843, and an epoch value of 300. To evaluate the performance of our proposed model, our proposed model is compared with YOLOv3 and faster RCNN. The results show that our proposed model performs better than YOLOv3 and faster RCNN with 96% mAP, 93.50% MCC value, 96.50% accuracy, 0.04 FPR, and 0.03 FNR value. The results show that our suggested model successfully identifies and classifies breast tumors while also overcoming previous research limitations by lowering the FPR and FNR and boosting the MCC value.
Topics: Breast; Breast Neoplasms; Computational Biology; Databases, Factual; Diagnosis, Computer-Assisted; False Negative Reactions; False Positive Reactions; Female; Humans; Machine Learning; Mammography; Neural Networks, Computer; Radiographic Image Enhancement; Sensitivity and Specificity
PubMed: 35027940
DOI: 10.1155/2022/1359019 -
Head & Face Medicine Mar 2015Oral squamous cell carcinoma (OSCC) has a remarkably high incidence worldwide, and a fairly serious prognosis, encouraging further research into advanced technologies... (Review)
Review
BACKGROUND
Oral squamous cell carcinoma (OSCC) has a remarkably high incidence worldwide, and a fairly serious prognosis, encouraging further research into advanced technologies for noninvasive methods of making early diagnoses, ideally in primary care settings.
OBJECTIVES
Our purpose was to examine the validity of using advanced noninvasive technologies in diagnosis of OSCC by identifying and evaluating relevant published reports.
DATA SOURCE
MEDLINE, EMBASE, and CINAHL were searched to identify clinical trials and other information published between 1990 and 10 June 2014; the searches of MEDLINE and EMBASE were updated to November 2014.
STUDY SELECTION
Studies of noninvasive methods of diagnosing OSCC, including oral brush biopsy, optical biopsy, saliva-based oral cancer diagnosis, and others were included.
DATA EXTRACTION
Data were abstracted and evaluated in duplicate for possible relevance on two occasions at an interval of 2 months before being included or excluded.
DATA SYNTHESIS
This study identified 163 studies of noninvasive methods for diagnosing OSCC that met the inclusion criteria. These included six studies of oral brush biopsy, 42 of saliva-based oral diagnosis, and 115 of optical biopsy. Sixty nine of these studies were assessed by the modified version of the QUADAS instrument. Saliva-based oral cancer diagnosis and optical biopsy were found to be promising noninvasive methods for diagnosing OSCC.
LIMITATION
The strength of evidence was rated low for accuracy outcomes because the studies did not report important details required to assess the risk for bias.
CONCLUSIONS
It is clear that screening for and early detection of cancer and pre-cancerous lesions have the potential to reduce the morbidity and mortality of this disease. Advances in technologies for saliva-based oral diagnosis and optical biopsy are promising pathways for the future development of more effective noninvasive methods for diagnosing OSCC that are easy to perform clinically in primary care settings.
Topics: Biopsy; Carcinoma, Squamous Cell; Diagnostic Tests, Routine; Female; Humans; Male; Mouth Neoplasms; Physical Examination; Saliva; Visual Analog Scale
PubMed: 25889859
DOI: 10.1186/s13005-015-0063-z -
Chronic Respiratory Disease 2021Asthma is a common, chronic, and heterogeneous disease with a global impact and substantial economic costs. It is also associated with significant mortality and... (Review)
Review
Asthma is a common, chronic, and heterogeneous disease with a global impact and substantial economic costs. It is also associated with significant mortality and morbidity and the burden of undiagnosed asthma is significant. Asthma can be difficult to diagnose as there is no gold standard test and, while spirometry is central in diagnosing asthma, it may not be sufficient to confirm or exclude the diagnosis. The most commonly reported spirometric measures (forced expiratory volume in one second (FEV) and forced vital capacity assess function in the larger airways. However, small airway dysfunction is highly prevalent in asthma and some studies suggest small airway involvement is one of the earliest disease manifestations. Moreover, there are new inhaled therapies with ultrafine particles that are specifically designed to target the small airways. Potentially, tests of small airways may more accurately diagnose early or mild asthma and assess the response to treatment than spirometry. Furthermore, some assessment techniques do not rely on forced ventilatory manoeuvres and may, therefore, be easier for certain groups to perform. This review discusses the current evidence of small airways tests in asthma and future research that may be needed to further assess their utility.
Topics: Asthma; Forced Expiratory Volume; Humans; Respiratory Function Tests; Spirometry; Vital Capacity
PubMed: 34693751
DOI: 10.1177/14799731211053332 -
Neuroscience Mar 2022This study investigates the error processing components in the EEG signal of Performers and Observers using an auditory lexical decision task, in which participants...
This study investigates the error processing components in the EEG signal of Performers and Observers using an auditory lexical decision task, in which participants heard spoken items and decided for each item if it was a real word or not. Pairs of participants were tested in both the role of the Performer and the Observer. In the literature, an Error Related Negativity (ERN)-Error Positivity (Pe) complex has been identified for performed (ERN-Pe) and observed (oERN-oPe) errors. While these effects have been widely studied for performance errors in speeded decision tasks relying on visual input, relatively little is known about the performance monitoring signatures in observed language processing based on auditory input. In the lexical decision task, native Dutch speakers listened to real Dutch Words, Non-Words, and crucially, long Pseudowords that resembled words until the final syllable and were shown to be error-prone in a pilot study, because they were responded to too soon. We hypothesised that the errors in the task would result in a response locked ERN-Pe pattern both for the Performer and for the Observer. Our hypothesis regarding the ERN was not supported, however a Pe-like effect, as well as a P300 were present. Analyses to disentangle lexical and error processing similarly indicated a P300 for errors, and the results furthermore pointed to differences between responses before and after word offset. The findings are interpreted as marking attention during error processing during auditory word recognition.
Topics: Attention; Electroencephalography; Evoked Potentials; Humans; Language; Pilot Projects; Reaction Time
PubMed: 33577954
DOI: 10.1016/j.neuroscience.2021.02.001