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WMJ : Official Publication of the State... May 2024Blastomycosis is a fungal infection caused by Blastomyces dermatitidis that is hyperendemic in Wisconsin. It commonly presents as a pulmonary infection and frequently...
INTRODUCTION
Blastomycosis is a fungal infection caused by Blastomyces dermatitidis that is hyperendemic in Wisconsin. It commonly presents as a pulmonary infection and frequently disseminates to the skin. Studies evaluating the presentation and diagnosis of blastomycosis with skin as a presenting sign have not been thoroughly evaluated, and understanding the most accurate way to diagnose this infection is important for earlier therapeutic intervention.
METHODS
This is a retrospective chart review study of a single institution. Subjects were identified through a search of ICD-9 () and ICD-10 () codes for blastomycosis in the clinical record and pathology database. Patients were included if diagnosed with cutaneous blastomycosis infection or involvement of the skin from systemic infection from January 1, 2009, to June 1, 2021.
RESULTS
Twenty patients with a diagnosis of cutaneous involvement of blastomycosis were identified; 65% (n = 13) were male. Median age of diagnosis was 55.5 years. Fifty-five percent of patients were White, 35% were Black or African American. In addition to residence in an endemic area, 50% (n = 10) had exposure risk factors. Fifty percent of patients (n = 10) initially presented with a skin concerns; 65% (n = 13) had extracutaneous involvement. Diagnosis was made by histopathology alone in 55% (n = 11), culture plus histopathology in 35% (n = 7), and culture alone in 5% (n = 1) of cases.
CONCLUSIONS
Our study highlighted similarities to those previously performed. Half of the patients (n = 10) who had cutaneous involvement of blastomycosis did not demonstrate clinically significant pulmonary involvement. Histopathology and culture remain critical in diagnosing cutaneous blastomycosis.
Topics: Humans; Wisconsin; Blastomycosis; Male; Female; Retrospective Studies; Middle Aged; Adult; Aged; Risk Factors; Blastomyces
PubMed: 38718236
DOI: No ID Found -
Dento Maxillo Facial Radiology Apr 2024Dental imaging plays a key role in the diagnosis and treatment of dental conditions, yet limitations regarding the quality and resolution of dental radiographs sometimes...
OBJECTIVES
Dental imaging plays a key role in the diagnosis and treatment of dental conditions, yet limitations regarding the quality and resolution of dental radiographs sometimes hinder precise analysis. Super-resolution with deep learning refers to a set of techniques used to enhance the resolution of images beyond their original size or quality using deep neural networks instead of traditional image interpolation methods which often result in blurred or pixelated images when attempting to increase resolution. Leveraging advancements in technology, this study aims to enhance the resolution of dental panoramic radiographs, thereby enabling more accurate diagnoses and treatment planning.
METHODS
About 1714 panoramic radiographs from 3 different open datasets are used for training (n = 1364) and testing (n = 350). The state of the art 4 different models is explored, namely Super-Resolution Convolutional Neural Network (SRCNN), Efficient Sub-Pixel Convolutional Neural Network, Super-Resolution Generative Adversarial Network, and Autoencoder. Performances in reconstructing high-resolution dental images from low-resolution inputs with different scales (s = 2, 4, 8) are evaluated by 2 well-accepted metrics Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR).
RESULTS
SSIM spans between 0.82 and 0.98 while PSNR are between 28.7 and 40.2 among all scales and models. SRCNN provides the best performance. Additionally, it is observed that performance decreased when images are scaled with higher values.
CONCLUSION
The findings highlight the potential of super-resolution concepts to significantly improve the quality and detail of dental panoramic radiographs, thereby contributing to enhanced interpretability.
Topics: Radiography, Panoramic; Humans; Neural Networks, Computer; Deep Learning; Signal-To-Noise Ratio
PubMed: 38483289
DOI: 10.1093/dmfr/twae009 -
Journal of Yeungnam Medical Science Oct 2023Recently, the International Working Group on the Diabetic Foot and the Infectious Diseases Society of America divided diabetic foot disease into diabetic foot infection...
Recently, the International Working Group on the Diabetic Foot and the Infectious Diseases Society of America divided diabetic foot disease into diabetic foot infection (DFI) and diabetic foot osteomyelitis (DFO). DFI is usually diagnosed clinically, while numerous methods exist to diagnose DFO. In this narrative review, the authors aim to summarize the updated data on the diagnosis of DFO. An extensive literature search using "diabetic foot [MeSH]" and "osteomyelitis [MeSH]" or "diagnosis" was performed using PubMed and Google Scholar in July 2023. The possibility of DFO is based on inflammatory clinical signs, including the probe-to-bone (PTB) test. Elevated inflammatory biochemical markers, especially erythrocyte sedimentation rate, are beneficial. Distinguishing abnormal findings of plain radiographs is also a first-line approach. Moreover, sophisticated modalities, including magnetic resonance imaging and nuclear medicine imaging, are helpful if doubt remains after a first-line diagnosis. Transcutaneous bone biopsy, which does not pass through the wound, is necessary to avoid contaminating the sample. This review focuses on the current diagnostic techniques for DFOs with an emphasis on the updates. To obtain the correct therapeutic results, selecting a proper option is necessary. Based on these numerous diagnosis modalities and indications, the proper choice of diagnostic tool can have favorable treatment outcomes.
PubMed: 37822082
DOI: 10.12701/jyms.2023.00976 -
Lung Cancer (Amsterdam, Netherlands) Dec 2023For diagnosing left adrenal gland metastasis in lung cancer, clinical guidelines recommend to perform EUS, but EUS-B (EUS using an EBUS-scope) is increasingly being... (Meta-Analysis)
Meta-Analysis
OBJECTIVES
For diagnosing left adrenal gland metastasis in lung cancer, clinical guidelines recommend to perform EUS, but EUS-B (EUS using an EBUS-scope) is increasingly being used. We evaluated the diagnostic performance of both procedures.
MATERIALS AND METHODS
We did a systematic review (PROSPERO, CRD42023416205) and searched MEDLINE and EMBASE on 04-July-2023 for studies evaluating EUS and/or EUS-B in diagnosing left adrenal gland metastases in adults with (suspected) lung cancer. Outcomes were: (1) ability to visualize the left adrenal gland, (2) ability to sample (in those with successful visualization and in whom sampling was attempted), (3) ability to obtain adequate material (in those with successful sampling), (4) malignancy detection rate (in those with successful sampling), and (5) remaining risk of malignancy (in those with a negative EUS(-B)-FNA and undergoing a reference standard). We performed random-effects meta-analyses.
RESULTS
We included 19 studies (EUS: n = 11, EUS-B: n = 6, both: n = 2), covering 1712 patients. All studies had high (n = 18) or unclear (n = 1) risk of bias (QUADAS-2). Average ability to visualize the left adrenal gland was 0.94 (95 %CI 0.82-0.98; n = 7 studies). Average ability to sample was 1.00 (95 %CI 0.99-1.00; n = 9). Average ability to obtain adequate material was 0.96 (95 %CI 0.93-0.98; n = 18). Average malignancy detection rate was 0.42 (95 %CI 0.34-0.49; n = 18). Remaining risk of malignancy was 0.07 (95 %CI 0.04-0.12; n = 8). Ability to visualize was slightly higher for EUS (0.99; 95 %CI 0.90-1.00) than EUS-B (0.84; 95 %CI 0.70-0.92; p = 0.025), but the other performance characteristics were similar. No major complications were reported.
CONCLUSION
Both EUS and EUS-B have good performance and are safe for left adrenal gland analysis in patients with lung cancer, but the number of high-quality studies is limited and further well-constructed prospective studies are needed.
Topics: Adult; Humans; Lung Neoplasms; Endoscopic Ultrasound-Guided Fine Needle Aspiration; Endosonography; Sensitivity and Specificity; Adrenal Glands; Adrenal Gland Neoplasms
PubMed: 37827042
DOI: 10.1016/j.lungcan.2023.107391 -
World Journal of Surgical Oncology Feb 2024The application of machine learning (ML) for identifying early gastric cancer (EGC) has drawn increasing attention. However, there lacks evidence-based support for its... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
The application of machine learning (ML) for identifying early gastric cancer (EGC) has drawn increasing attention. However, there lacks evidence-based support for its specific diagnostic performance. Hence, this systematic review and meta-analysis was implemented to assess the performance of image-based ML in EGC diagnosis.
METHODS
We performed a comprehensive electronic search in PubMed, Embase, Cochrane Library, and Web of Science up to September 25, 2022. QUADAS-2 was selected to judge the risk of bias of included articles. We did the meta-analysis using a bivariant mixed-effect model. Sensitivity analysis and heterogeneity test were performed.
RESULTS
Twenty-one articles were enrolled. The sensitivity (SEN), specificity (SPE), and SROC of ML-based models were 0.91 (95% CI: 0.87-0.94), 0.85 (95% CI: 0.81-0.89), and 0.94 (95% CI: 0.39-1.00) in the training set and 0.90 (95% CI: 0.86-0.93), 0.90 (95% CI: 0.86-0.92), and 0.96 (95% CI: 0.19-1.00) in the validation set. The SEN, SPE, and SROC of EGC diagnosis by non-specialist clinicians were 0.64 (95% CI: 0.56-0.71), 0.84 (95% CI: 0.77-0.89), and 0.80 (95% CI: 0.29-0.97), and those by specialist clinicians were 0.80 (95% CI: 0.74-0.85), 0.88 (95% CI: 0.85-0.91), and 0.91 (95% CI: 0.37-0.99). With the assistance of ML models, the SEN of non-specialist physicians in the diagnosis of EGC was significantly improved (0.76 vs 0.64).
CONCLUSION
ML-based diagnostic models have greater performance in the identification of EGC. The diagnostic accuracy of non-specialist clinicians can be improved to the level of the specialists with the assistance of ML models. The results suggest that ML models can better assist less experienced clinicians in diagnosing EGC under endoscopy and have broad clinical application value.
Topics: Humans; Stomach Neoplasms; Endoscopy; Machine Learning
PubMed: 38297303
DOI: 10.1186/s12957-024-03321-9 -
BMC Medical Informatics and Decision... Sep 2023One of the most common sleep disorders is sleep apnea syndrome. To diagnose sleep apnea syndrome, polysomnography is typically used, but it has limitations in terms of...
BACKGROUND
One of the most common sleep disorders is sleep apnea syndrome. To diagnose sleep apnea syndrome, polysomnography is typically used, but it has limitations in terms of labor, cost, and time. Therefore, studies have been conducted to develop automated detection algorithms using limited biological signals that can be more easily diagnosed. However, the lack of information from limited signals can result in uncertainty from artificial intelligence judgments. Therefore, we performed selective prediction by using estimated respiratory signals from electrocardiogram and oxygen saturation signals based on confidence scores to classify only those sleep apnea occurrence samples with high confidence. In addition, for samples with high uncertainty, this algorithm rejected them, providing a second opinion to the clinician.
METHOD
Our developed model utilized polysomnography data from 994 subjects obtained from Massachusetts General Hospital. We performed feature extraction from the latent vector using the autoencoder. Then, one dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) was designed and trained to measure confidence scores for input, with an additional selection function. We set a confidence score threshold called the target coverage and performed optimization only on samples with confidence scores higher than the target coverage. As a result, we demonstrated that the empirical coverage trained in the model converged to the target coverage.
RESULT
To confirm whether the model has been optimized according to the objectives, the coverage violation was used to measure the difference between the target coverage and the empirical coverage. As a result, the value of coverage violation was found to be an average of 0.067. Based on the model, we evaluated the classification performance of sleep apnea and confirmed that it achieved 90.26% accuracy, 91.29% sensitivity, and 89.21% specificity. This represents an improvement of approximately 7.03% in all metrics compared to the performance achieved without using a selective prediction.
CONCLUSION
This algorithm based on selective prediction utilizes confidence measurement method to minimize the problem caused by limited biological information. Based on this approach, this algorithm is applicable to wearable devices despite low signal quality and can be used as a simple detection method that determine the need for polysomnography or complement it.
Topics: Humans; Artificial Intelligence; Algorithms; Benchmarking; Electrocardiography; Sleep Apnea Syndromes
PubMed: 37735681
DOI: 10.1186/s12911-023-02292-3 -
Australian Journal of General Practice Jun 2024General practitioners manage a significant proportion of inflammatory and neoplastic skin conditions on a daily basis. Various surgical techniques can be employed to aid...
BACKGROUND
General practitioners manage a significant proportion of inflammatory and neoplastic skin conditions on a daily basis. Various surgical techniques can be employed to aid in diagnosis, including punch biopsies, shave biopsy, shave excision, incisional biopsy, curettage and formal excision with closure. Requiring minimal equipment, shave procedures are quick to perform, produce good cosmetic outcomes and minimise costs.
OBJECTIVE
Our aim is to discuss shave procedures in detail and highlight the difference between shave biopsies and shave excisions, as well as the role they each have in diagnosing an array of benign, inflammatory and malignant skin conditions, including melanocytic lesions.
DISCUSSION
Shave procedures performed on suitable lesions by trained practitioners can be used for sampling or removing suspect lesions. Where the intent is complete removal, margin involvement is rare given good lesion selection and technique.
Topics: Humans; General Practice; Biopsy; Skin Diseases
PubMed: 38840380
DOI: 10.31128/AJGP-06-23-6872 -
Medicina (Kaunas, Lithuania) Nov 2023: although musculoskeletal alterations are common in patients with Down syndrome (DS), studies investigating this association are scarce, and proposals for diagnostic...
: although musculoskeletal alterations are common in patients with Down syndrome (DS), studies investigating this association are scarce, and proposals for diagnostic standardization are limited. We aimed to evaluate the prevalence of musculoskeletal disorders in the lower limbs in a sample of children and adolescents with DS and to investigate the diagnostic capacity of orthopedic clinical examinations performed by orthopedists and pediatricians to diagnose these alterations. : Twenty-two patients aged between three and ten years with DS were included. Patients and guardians answered a simple questionnaire regarding orthopedic complaints and underwent a systematic orthopedic physical examination, performed twice: once by an orthopedist and again by a pediatrician. Patients underwent a series of radiographs to diagnose anisomelia, hip dysplasia, epiphysiolysis, flatfoot valgus, mechanical axis varus, and mechanical axis valgus. The radiological diagnosis was considered the gold standard, and the diagnostic capacity of the physical examination performed by each physician was determined. : The median age was 6.50 years. Only four patients (18.2%) presented with orthopedic complaints. All patients were diagnosed with at least one musculoskeletal disorder. The only musculoskeletal disorder with a good diagnostic capacity was flatfoot valgus. Limited sensitivity values were found for hip dysplasia, mechanical axis varus, and mechanical axis valgus. The agreement between the orthopedic physical examinations performed by the two examiners was weak, poor, or indeterminate for most of the analyzed items. : There was a high prevalence of orthopedic alterations in children with DS who did not present with musculoskeletal complaints. The diagnostic capacity of the physical examination was limited. Therefore, all children with DS should undergo a radiological evaluation of the musculoskeletal system and subsequent specialized orthopedic evaluation. Level of Evidence: Level II (Diagnostic Studies).
Topics: Adolescent; Humans; Child; Child, Preschool; Flatfoot; Down Syndrome; Hip Dislocation; Lower Extremity; Musculoskeletal Diseases; Hip Dislocation, Congenital; Physical Examination
PubMed: 38004035
DOI: 10.3390/medicina59111986 -
PloS One 2023The inspection of stained tissue slides by pathologists is essential for the early detection, diagnosis and monitoring of disease. Recently, deep learning methods for...
The inspection of stained tissue slides by pathologists is essential for the early detection, diagnosis and monitoring of disease. Recently, deep learning methods for the analysis of whole-slide images (WSIs) have shown excellent performance on these tasks, and have the potential to substantially reduce the workload of pathologists. However, WSIs present a number of unique challenges for analysis, requiring special consideration of image annotations, slide and image artefacts, and evaluation of WSI-trained model performance. Here we introduce SliDL, a Python library for performing pre- and post-processing of WSIs. SliDL makes WSI data handling easy, allowing users to perform essential processing tasks in a few simple lines of code, bridging the gap between standard image analysis and WSI analysis. We introduce each of the main functionalities within SliDL: from annotation and tile extraction to tissue detection and model evaluation. We also provide 'code snippets' to guide the user in running SliDL. SliDL has been designed to interact with PyTorch, one of the most widely used deep learning libraries, allowing seamless integration into deep learning workflows. By providing a framework in which deep learning methods for WSI analysis can be developed and applied, SliDL aims to increase the accessibility of an important application of deep learning.
Topics: Deep Learning; Image Interpretation, Computer-Assisted; Coloring Agents; Image Processing, Computer-Assisted
PubMed: 37549131
DOI: 10.1371/journal.pone.0289499 -
Medical Ultrasonography Mar 2024The aim of this study is to investigate the diagnostic performances of Ultrasonography (US), Shear-wave Elastography (SWE), and Superb Microvascular Imaging (SMI)...
AIMS
The aim of this study is to investigate the diagnostic performances of Ultrasonography (US), Shear-wave Elastography (SWE), and Superb Microvascular Imaging (SMI) findings in the diagnosis of malignant thyroid nodules (MTNs) and to determine the US algorithm with the best diagnostic performance.
MATERIAL AND METHODS
Eighty-one nodules in 77 patients who had underwent multimodal US with biopsy results, were evaluated. Echogenicity, nodule components, contours, presence and type of calcification, and size were analyzed with US. Nodule stiffness and vascular index (VI) measurements were performed via SWE and SMI. The power of the US algorithm in predicting malignancy was evaluated.
RESULTS
Hypoechogenicity, irregular contour, aspect ratio (anteroposterior (AP)/transvers diameter) >1, and >43.9 kPa were the characteristicshad significant efficacy in the diagnosis of MTNs. Sensitivity, specificity, and AUC values were respectively 100%, 48.5%, and 0.742 for hypoechogenicity; 80%, 90.1%, and 0.855 for irregular contour; 60%, 71.2%, and 0.656 for aspect ratio >1; 60%, 72.7%, and 0.671 for >43.9 kPa; and 93.3%, 90.9%, and 0.921 for the US algorithm. VI did not show significant efficacy in diagnosis.
CONCLUSION
Some B-mode and SWE findings showed sufficient efficacy in differentiating benign and malign nodules on their own. However, diagnostic accuracy increased significantly when the US algorithm was applied.
Topics: Humans; Thyroid Nodule; Sensitivity and Specificity; Reproducibility of Results; Ultrasonography; Elasticity Imaging Techniques; Biopsy, Fine-Needle; Algorithms
PubMed: 38537188
DOI: 10.11152/mu-4325