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Hand Clinics Feb 2024Nonunion is a common and costly problem. Unfortunately, there is no widely agreed upon and standardized definition for nonunion. The evaluation of bony union should... (Review)
Review
Nonunion is a common and costly problem. Unfortunately, there is no widely agreed upon and standardized definition for nonunion. The evaluation of bony union should start with a thorough history and physical examination. The clinician should consider patient-dependent as well as patient-independent characteristics that may influence the rate of healing and evaluate the patient for physical examination findings suggestive of bony union and infection. Radiographs and clinical examination can help confirm a diagnosis of union. When the diagnosis is in doubt, however, advanced imaging modalities as well as laboratory studies can help a surgeon determine when further intervention is necessary.
Topics: Humans; Fractures, Ununited; Fracture Healing; Radiography; Retrospective Studies
PubMed: 37979981
DOI: 10.1016/j.hcl.2023.06.001 -
Journal of Integrative Neuroscience Feb 2024In the initial assessment of a headache patient, several dangerous secondary etiologies must be considered. A thorough history and physical examination, along with a... (Review)
Review
In the initial assessment of a headache patient, several dangerous secondary etiologies must be considered. A thorough history and physical examination, along with a comprehensive differential diagnosis may alert a physician to the diagnosis of a secondary headache particularly when it is accompanied by certain clinical features. Evaluation and workup include a complete neurological examination, consideration of neuroimaging, and serum/spinal fluid analysis if indicated. Careful attention to the patients' history and physical examination will guide the diagnostic work-up and management. In this review, we summarize the diagnostic workup of various primary and secondary headache etiologies. Although most headaches are primary in nature, it is essential to screen for headache "red flags", as they can suggest life threatening secondary etiologies. When secondary causes are suspected, appropriate neuroimaging can further differentiate the underlying cause. The appropriate imaging is dependent on the most likely secondary etiology, which is deduced from history and physical examination. When no red flags are present, primary headaches are more likely. These can be differentiated by frequency, location, duration, triggers, and presence of aura. The different clinical presentations for secondary headaches, as well as the distinguishing features for primary headaches are outlined in this review.
Topics: Humans; Headache Disorders; Headache; Neuroimaging; Diagnosis, Differential
PubMed: 38419454
DOI: 10.31083/j.jin2302043 -
BMC Oral Health Sep 2023Intra-oral scans and gypsum cast scans (OS) are widely used in orthodontics, prosthetics, implantology, and orthognathic surgery to plan patient-specific treatments,...
OBJECTIVE
Intra-oral scans and gypsum cast scans (OS) are widely used in orthodontics, prosthetics, implantology, and orthognathic surgery to plan patient-specific treatments, which require teeth segmentations with high accuracy and resolution. Manual teeth segmentation, the gold standard up until now, is time-consuming, tedious, and observer-dependent. This study aims to develop an automated teeth segmentation and labeling system using deep learning.
MATERIAL AND METHODS
As a reference, 1750 OS were manually segmented and labeled. A deep-learning approach based on PointCNN and 3D U-net in combination with a rule-based heuristic algorithm and a combinatorial search algorithm was trained and validated on 1400 OS. Subsequently, the trained algorithm was applied to a test set consisting of 350 OS. The intersection over union (IoU), as a measure of accuracy, was calculated to quantify the degree of similarity between the annotated ground truth and the model predictions.
RESULTS
The model achieved accurate teeth segmentations with a mean IoU score of 0.915. The FDI labels of the teeth were predicted with a mean accuracy of 0.894. The optical inspection showed excellent position agreements between the automatically and manually segmented teeth components. Minor flaws were mostly seen at the edges.
CONCLUSION
The proposed method forms a promising foundation for time-effective and observer-independent teeth segmentation and labeling on intra-oral scans.
CLINICAL SIGNIFICANCE
Deep learning may assist clinicians in virtual treatment planning in orthodontics, prosthetics, implantology, and orthognathic surgery. The impact of using such models in clinical practice should be explored.
Topics: Humans; Deep Learning; Algorithms; Calcium Sulfate; Dental Care; Physical Examination
PubMed: 37670290
DOI: 10.1186/s12903-023-03362-8 -
The Medical Clinics of North America Jul 2024Allergy to penicillin can occur via any of the 4 types of Gel-Coombs hypersensitivity reactions, producing distinct clinical histories and physical examination findings.... (Review)
Review
Allergy to penicillin can occur via any of the 4 types of Gel-Coombs hypersensitivity reactions, producing distinct clinical histories and physical examination findings. Treatments include penicillin discontinuation, and depending on the type of reaction, epinephrine, antihistamines, and/or glucocorticoids. Most beta-lactams may be safely used in penicillin-allergic patients, with the possible exception of first-generation and second-generation cephalosporins. Penicillin testing includes skin testing, patch testing, and graded challenge. The selection of the type of testing depends on the clinical setting, equipment availability, and type of hypersensitivity reaction. Desensitization may be used in some cases where treatment with penicillins is essential.
Topics: Humans; Penicillins; Drug Hypersensitivity; Anti-Bacterial Agents; Skin Tests; Epinephrine; Patch Tests
PubMed: 38816110
DOI: 10.1016/j.mcna.2023.08.009 -
Circulation Sep 2023
Topics: Humans; Blood Pressure
PubMed: 37747956
DOI: 10.1161/CIRCULATIONAHA.123.066073 -
Nutrition in Clinical Practice :... Oct 2023Undernutrition is highly prevalent in children who are critically ill and is associated with increased morbidity and mortality, including a higher risk of infection due... (Review)
Review
Undernutrition is highly prevalent in children who are critically ill and is associated with increased morbidity and mortality, including a higher risk of infection due to transitory immunological disorders, inadequate wound healing, reduced gut function, longer dependency on mechanical ventilation, and longer hospital stays compared with eutrophic children who are critically ill. Nutrition care studies have proposed that early interventions targeting nutrition assessment can prevent or minimize the complications of undernutrition. Stress promotes an acute inflammatory response mediated by cytokines, resulting in increased basal metabolism and nitrogen excretion and leading to muscle loss and changes in body composition. Therefore, the inclusion of body composition assessment is important in the evaluation of these patients because, in addition to the nutrition aspect, body composition seems to predict clinical prognosis. Several techniques can be used to assess body composition, such as arm measurements, calf circumference, grip strength, bioelectrical impedance analysis, and imaging examinations, including computed tomography and dual-energy x-ray absorptiometry. This review of available evidence suggests that arm measurements seem to be well-established in assessing body composition in children who are critically ill, and that bioelectrical impedance analysis with phase angle, handgrip strength, calf circumference and ultrasound seem to be promising in this evaluation. However, further robust studies based on scientific evidence are necessary.
Topics: Humans; Child; Critical Illness; Hand Strength; Absorptiometry, Photon; Body Composition; Malnutrition
PubMed: 37721465
DOI: 10.1002/ncp.11061 -
The Cochrane Database of Systematic... Nov 2023Keratoconus remains difficult to diagnose, especially in the early stages. It is a progressive disorder of the cornea that starts at a young age. Diagnosis is based on... (Review)
Review
BACKGROUND
Keratoconus remains difficult to diagnose, especially in the early stages. It is a progressive disorder of the cornea that starts at a young age. Diagnosis is based on clinical examination and corneal imaging; though in the early stages, when there are no clinical signs, diagnosis depends on the interpretation of corneal imaging (e.g. topography and tomography) by trained cornea specialists. Using artificial intelligence (AI) to analyse the corneal images and detect cases of keratoconus could help prevent visual acuity loss and even corneal transplantation. However, a missed diagnosis in people seeking refractive surgery could lead to weakening of the cornea and keratoconus-like ectasia. There is a need for a reliable overview of the accuracy of AI for detecting keratoconus and the applicability of this automated method to the clinical setting.
OBJECTIVES
To assess the diagnostic accuracy of artificial intelligence (AI) algorithms for detecting keratoconus in people presenting with refractive errors, especially those whose vision can no longer be fully corrected with glasses, those seeking corneal refractive surgery, and those suspected of having keratoconus. AI could help ophthalmologists, optometrists, and other eye care professionals to make decisions on referral to cornea specialists. Secondary objectives To assess the following potential causes of heterogeneity in diagnostic performance across studies. • Different AI algorithms (e.g. neural networks, decision trees, support vector machines) • Index test methodology (preprocessing techniques, core AI method, and postprocessing techniques) • Sources of input to train algorithms (topography and tomography images from Placido disc system, Scheimpflug system, slit-scanning system, or optical coherence tomography (OCT); number of training and testing cases/images; label/endpoint variable used for training) • Study setting • Study design • Ethnicity, or geographic area as its proxy • Different index test positivity criteria provided by the topography or tomography device • Reference standard, topography or tomography, one or two cornea specialists • Definition of keratoconus • Mean age of participants • Recruitment of participants • Severity of keratoconus (clinically manifest or subclinical) SEARCH METHODS: We searched CENTRAL (which contains the Cochrane Eyes and Vision Trials Register), Ovid MEDLINE, Ovid Embase, OpenGrey, the ISRCTN registry, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP). There were no date or language restrictions in the electronic searches for trials. We last searched the electronic databases on 29 November 2022.
SELECTION CRITERIA
We included cross-sectional and diagnostic case-control studies that investigated AI for the diagnosis of keratoconus using topography, tomography, or both. We included studies that diagnosed manifest keratoconus, subclinical keratoconus, or both. The reference standard was the interpretation of topography or tomography images by at least two cornea specialists.
DATA COLLECTION AND ANALYSIS
Two review authors independently extracted the study data and assessed the quality of studies using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. When an article contained multiple AI algorithms, we selected the algorithm with the highest Youden's index. We assessed the certainty of evidence using the GRADE approach.
MAIN RESULTS
We included 63 studies, published between 1994 and 2022, that developed and investigated the accuracy of AI for the diagnosis of keratoconus. There were three different units of analysis in the studies: eyes, participants, and images. Forty-four studies analysed 23,771 eyes, four studies analysed 3843 participants, and 15 studies analysed 38,832 images. Fifty-four articles evaluated the detection of manifest keratoconus, defined as a cornea that showed any clinical sign of keratoconus. The accuracy of AI seems almost perfect, with a summary sensitivity of 98.6% (95% confidence interval (CI) 97.6% to 99.1%) and a summary specificity of 98.3% (95% CI 97.4% to 98.9%). However, accuracy varied across studies and the certainty of the evidence was low. Twenty-eight articles evaluated the detection of subclinical keratoconus, although the definition of subclinical varied. We grouped subclinical keratoconus, forme fruste, and very asymmetrical eyes together. The tests showed good accuracy, with a summary sensitivity of 90.0% (95% CI 84.5% to 93.8%) and a summary specificity of 95.5% (95% CI 91.9% to 97.5%). However, the certainty of the evidence was very low for sensitivity and low for specificity. In both groups, we graded most studies at high risk of bias, with high applicability concerns, in the domain of patient selection, since most were case-control studies. Moreover, we graded the certainty of evidence as low to very low due to selection bias, inconsistency, and imprecision. We could not explain the heterogeneity between the studies. The sensitivity analyses based on study design, AI algorithm, imaging technique (topography versus tomography), and data source (parameters versus images) showed no differences in the results.
AUTHORS' CONCLUSIONS
AI appears to be a promising triage tool in ophthalmologic practice for diagnosing keratoconus. Test accuracy was very high for manifest keratoconus and slightly lower for subclinical keratoconus, indicating a higher chance of missing a diagnosis in people without clinical signs. This could lead to progression of keratoconus or an erroneous indication for refractive surgery, which would worsen the disease. We are unable to draw clear and reliable conclusions due to the high risk of bias, the unexplained heterogeneity of the results, and high applicability concerns, all of which reduced our confidence in the evidence. Greater standardization in future research would increase the quality of studies and improve comparability between studies.
Topics: Humans; Artificial Intelligence; Keratoconus; Cross-Sectional Studies; Physical Examination; Case-Control Studies
PubMed: 37965960
DOI: 10.1002/14651858.CD014911.pub2 -
Frontiers in Endocrinology 2023Sarcopenic obesity (SO) is defined as obesity with low skeletal muscle function and mass. This study aimed to evaluate the presence of sarcopenic obesity according to...
INTRODUCTION
Sarcopenic obesity (SO) is defined as obesity with low skeletal muscle function and mass. This study aimed to evaluate the presence of sarcopenic obesity according to different diagnostic criteria and assess the elements of sarcopenia in children and adolescents with obesity.
METHODS
A total of 95 children and adolescents with obesity (diagnosed with the use of International Obesity Task Force (IOTF) criteria) with a mean age of 12.7( ± 3) years participated in the study. Body composition was assessed with the use of bioelectrical impedance-BIA (Tanita BC480MA) and dual-energy X-ray absorptiometry-DXA (Hologic). Fat mass (FM) and appendicular skeletal muscle mass (SMMa) were expressed as kilograms (kg) and percentage (%). Muscle-to-fat ratio (MFR) was defined as SMMa divided by FM. A dynamometer was used in order to measure grip strength. Six-minute walk test (6MWT) and a timed up-and-go test (TUG) were used to assess physical performance.
RESULTS
The presence of SO ranged from 6.32% to 97.89%, depending on the criteria used to define sarcopenia. Children with sarcopenia, defined as a co- occurrence of low skeletal muscle mass % (SMM%) measured by DXA (≤9th centile) according to McCarthy et al. and weak handgrip strength (≤10th centile) according to Dodds et al., had significantly lower SMMa measured by both DXA and BIA, lower maximal handgrip strength, and lower physical performance. Maximal handgrip was positively correlated with SMMa (kg) and SMMa% derived from both DXA and BIA and BIA-MFR. Maximal handgrip was negatively correlated with waist-to-height ratio (WHtR). The distance of 6MWT correlated positively with BIA-measured SMMa% and BIA-MFR. 6MWT distance correlated negatively with BIA-FM% and body mass index (BMI) z-score. TUG was positively correlated with BIA-FM%, BMI z-score, WHtR, and IOTF categories and negatively correlated with BIA-SMMa% and BIA-MFR.
DISCUSSION
The presence of sarcopenia in our study varied depending on the diagnostic criteria used. This is one of the first studies evaluating muscle mass, muscle strength, and physical performance in children and adolescents with obesity. The study highlighted the need for the implementation of a consensus statement regarding SO diagnostic criteria in children and adolescents.
Topics: Adolescent; Humans; Child; Sarcopenia; Pediatric Obesity; Hand Strength; Absorptiometry, Photon; Muscle Strength; Muscle, Skeletal
PubMed: 37859982
DOI: 10.3389/fendo.2023.1252853