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Journal of Ultrasound Jun 2023Necrotizing fasciitis (NF) is a rapidly progressive necrosis of the fascial layer with a high mortality rate. It is a life-threatening medical emergency that requires... (Review)
Review
INTRODUCTION
Necrotizing fasciitis (NF) is a rapidly progressive necrosis of the fascial layer with a high mortality rate. It is a life-threatening medical emergency that requires urgent treatment. Lack of skin finding in NF made diagnosis difficult and required a high clinical index of suspicion. The use of ultrasound may guide clinicians in improving diagnostic speed and accuracy, thus leading to improved management decisions and patient outcomes. This literature search aims to review the use of point-of-care ultrasonography in diagnosing necrotizing fasciitis.
METHOD
We searched relevant electronic databases, including PUBMED, MEDLINE, and SCOPUS, and performed a systematic review. Keywords used were "necrotizing fasciitis" or "necrotising fasciitis" or "necrotizing soft tissue infections" and "point-of-care ultrasonography" "ultrasonography" or "ultrasound". No temporal limitation was set. An additional search was performed via google scholar, and the top 100 entry was screened.
RESULTS
Among 540 papers screened, only 21 were related to diagnosing necrotizing fasciitis using ultrasonography. The outcome includes three observational studies, 16 case reports, and two case series, covering the period from 1976 to 2022.
CONCLUSION
Although the use of ultrasonography in diagnosing NF was published in several papers with promising results, more studies are required to investigate its diagnostic accuracy and potential to reduce time delay before surgical intervention, morbidity, and mortality.
Topics: Humans; Point-of-Care Systems; Fasciitis, Necrotizing; Ultrasonography; Necrosis
PubMed: 36694072
DOI: 10.1007/s40477-022-00761-5 -
PloS One 2022Artificial intelligence (AI) algorithms have been applied to diagnose temporomandibular disorders (TMDs). However, studies have used different patient selection... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Artificial intelligence (AI) algorithms have been applied to diagnose temporomandibular disorders (TMDs). However, studies have used different patient selection criteria, disease subtypes, input data, and outcome measures. Resultantly, the performance of the AI models varies.
OBJECTIVE
This study aimed to systematically summarize the current literature on the application of AI technologies for diagnosis of different TMD subtypes, evaluate the quality of these studies, and assess the diagnostic accuracy of existing AI models.
MATERIALS AND METHODS
The study protocol was carried out based on the preferred reporting items for systematic review and meta-analysis protocols (PRISMA). The PubMed, Embase, and Web of Science databases were searched to find relevant articles from database inception to June 2022. Studies that used AI algorithms to diagnose at least one subtype of TMD and those that assessed the performance of AI algorithms were included. We excluded studies on orofacial pain that were not directly related to the TMD, such as studies on atypical facial pain and neuropathic pain, editorials, book chapters, and excerpts without detailed empirical data. The risk of bias was assessed using the QUADAS-2 tool. We used Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) to provide certainty of evidence.
RESULTS
A total of 17 articles for automated diagnosis of masticatory muscle disorders, TMJ osteoarthrosis, internal derangement, and disc perforation were included; they were retrospective studies, case-control studies, cohort studies, and a pilot study. Seven studies were subjected to a meta-analysis for diagnostic accuracy. According to the GRADE, the certainty of evidence was very low. The performance of the AI models had accuracy and specificity ranging from 84% to 99.9% and 73% to 100%, respectively. The pooled accuracy was 0.91 (95% CI 0.76-0.99), I2 = 97% (95% CI 0.96-0.98), p < 0.001.
CONCLUSIONS
Various AI algorithms developed for diagnosing TMDs may provide additional clinical expertise to increase diagnostic accuracy. However, it should be noted that a high risk of bias was present in the included studies. Also, certainty of evidence was very low. Future research of higher quality is strongly recommended.
Topics: Artificial Intelligence; Facial Pain; Humans; Pilot Projects; Retrospective Studies; Temporomandibular Joint Disorders
PubMed: 35980894
DOI: 10.1371/journal.pone.0272715 -
Folia Medica Cracoviensia Apr 2023Carcinoma of unknown primary (CUP) is a heterogeneous group of oncological diseases in which it is impossible to determine the primary tumor. The incidence is 3-5% of...
Carcinoma of unknown primary (CUP) is a heterogeneous group of oncological diseases in which it is impossible to determine the primary tumor. The incidence is 3-5% of oncologic patients, but the survival time varies from 6 weeks to 5 months. The diagnostics should begin with a clinical evaluation and basic laboratory tests. For CUP placed in head and neck the positron emission tomography - computed tomography is recommended; pancreatic or lung neoplasms are diagnosed with the computed tomography as well. Recently, the magnetic resonance, especially whole-body diffusion-weighted imaging has been introduced to the imaging panel. The lesion obtained during surgically removed metastases or biopsy material should be histopathological and molecularly examined to define the type of tumor. The basic immunoexpression panel should include cytokeratin-5/6, -7 and -20, EMA, synaptophysin, chromogranin, vimentin and GATA3 and molecular expression of ERBB2, PIK3CA, NF1, NF2, BRAF, IDH1, PTEN, FGFR2, EGFR, MET and CDK6. During the accurate diagnostics enable to classify malignancy of undefined primary origin as provisional CUP or finally confirmed CUP in which the primary place of tumor remains undetectable. The detailed diagnostics should be performed in highly specified centers to establish an accurate diagnosis and to initiate personalized treatment. Majority of patients are diagnosed with adenocarcinoma (70%), undifferentiated carcinoma (20%), squamous cell or transitional cell/uroepithelial carcinoma (5-10%), neuroendocrine tumor (5%) and with minor incidence other histological types, including melanoma.
Topics: Humans; Neoplasms, Unknown Primary; Carcinoma; Adenocarcinoma; Tomography, X-Ray Computed; Magnetic Resonance Imaging; Head and Neck Neoplasms
PubMed: 37406274
DOI: 10.24425/fmc.2023.145427 -
Diseases of the Esophagus : Official... Nov 2023Early detection of esophageal cancer is limited by accurate endoscopic diagnosis of subtle macroscopic lesions. Endoscopic interpretation is subject to expertise,... (Meta-Analysis)
Meta-Analysis
Early detection of esophageal cancer is limited by accurate endoscopic diagnosis of subtle macroscopic lesions. Endoscopic interpretation is subject to expertise, diagnostic skill, and thus human error. Artificial intelligence (AI) in endoscopy is increasingly bridging this gap. This systematic review and meta-analysis consolidate the evidence on the use of AI in the endoscopic diagnosis of esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE and Ovid EMBASE databases and articles on the role of AI in the endoscopic diagnosis of esophageal cancer management were included. A meta-analysis was also performed. Fourteen studies (1590 patients) assessed the use of AI in endoscopic diagnosis of esophageal squamous cell carcinoma-the pooled sensitivity and specificity were 91.2% (84.3-95.2%) and 80% (64.3-89.9%). Nine studies (478 patients) assessed AI capabilities of diagnosing esophageal adenocarcinoma with the pooled sensitivity and specificity of 93.1% (86.8-96.4) and 86.9% (81.7-90.7). The remaining studies formed the qualitative summary. AI technology, as an adjunct to endoscopy, can assist in accurate, early detection of esophageal malignancy. It has shown superior results to endoscopists alone in identifying early cancer and assessing depth of tumor invasion, with the added benefit of not requiring a specialized skill set. Despite promising results, the application in real-time endoscopy is limited, and further multicenter trials are required to accurately assess its use in routine practice.
Topics: Humans; Esophageal Neoplasms; Esophageal Squamous Cell Carcinoma; Artificial Intelligence; Endoscopy; Adenocarcinoma
PubMed: 37480192
DOI: 10.1093/dote/doad048 -
Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review.Journal of Medical Internet Research Mar 2023Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment... (Review)
Review
BACKGROUND
Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer.
OBJECTIVE
This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature.
METHODS
A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively.
RESULTS
Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms.
CONCLUSIONS
This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.
Topics: Humans; Artificial Intelligence; Early Detection of Cancer; Pancreatic Neoplasms; Algorithms
PubMed: 37000507
DOI: 10.2196/44248 -
Journal of Clinical Pathology Jul 2021Digital pathology (DP) has the potential to fundamentally change the way that histopathology is practised, by streamlining the workflow, increasing efficiency, improving... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Digital pathology (DP) has the potential to fundamentally change the way that histopathology is practised, by streamlining the workflow, increasing efficiency, improving diagnostic accuracy and facilitating the platform for implementation of artificial intelligence-based computer-assisted diagnostics. Although the barriers to wider adoption of DP have been multifactorial, limited evidence of reliability has been a significant contributor. A meta-analysis to demonstrate the combined accuracy and reliability of DP is still lacking in the literature.
OBJECTIVES
We aimed to review the published literature on the diagnostic use of DP and to synthesise a statistically pooled evidence on safety and reliability of DP for routine diagnosis (primary and secondary) in the context of validation process.
METHODS
A comprehensive literature search was conducted through PubMed, Medline, EMBASE, Cochrane Library and Google Scholar for studies published between 2013 and August 2019. The search protocol identified all studies comparing DP with light microscopy (LM) reporting for diagnostic purposes, predominantly including H&E-stained slides. Random-effects meta-analysis was used to pool evidence from the studies.
RESULTS
Twenty-five studies were deemed eligible to be included in the review which examined a total of 10 410 histology samples (average sample size 176). For overall concordance (clinical concordance), the agreement percentage was 98.3% (95% CI 97.4 to 98.9) across 24 studies. A total of 546 major discordances were reported across 25 studies. Over half (57%) of these were related to assessment of nuclear atypia, grading of dysplasia and malignancy. These were followed by challenging diagnoses (26%) and identification of small objects (16%).
CONCLUSION
The results of this meta-analysis indicate equivalent performance of DP in comparison with LM for routine diagnosis. Furthermore, the results provide valuable information concerning the areas of diagnostic discrepancy which may warrant particular attention in the transition to DP.
Topics: Artificial Intelligence; Diagnosis, Computer-Assisted; Humans; Image Interpretation, Computer-Assisted; Microscopy; Pathology, Clinical
PubMed: 32934103
DOI: 10.1136/jclinpath-2020-206764 -
Journal of Obstetrics and Gynaecology :... Dec 2024The diagnosis of endometriomas in patients with endometriosis is of primary importance because it influences the management and prognosis of infertility and pain.... (Meta-Analysis)
Meta-Analysis Review
INTRODUCTION
The diagnosis of endometriomas in patients with endometriosis is of primary importance because it influences the management and prognosis of infertility and pain. Imaging techniques are evolving constantly. This study aimed to systematically assess the diagnostic accuracy of transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI) in detecting endometrioma using the surgical visualisation of lesions with or without histopathological confirmation as reference standards in patients of reproductive age with suspected endometriosis.
METHODS
PubMed, Embase, Web of Science, Cumulative Index to Nursing and Allied Health Literature and ClinicalTrials.gov databases were searched from their inception to 12 October 2022, using a manual search for additional articles. Two authors independently performed title, abstract and full-text screening of the identified records, extracted study details and quantitative data and assessed the quality of the studies using the 'Quality Assessment of Diagnostic Accuracy Study 2' tool. Bivariate random-effects models were used to determine the pooled sensitivity and specificity, compare the two imaging modalities and evaluate the sources of heterogeneity.
RESULTS
Sixteen prospective studies (10 assessing TVUS, 4 assessing MRI and 2 assessing both TVUS and MRI) were included, representing 1976 participants. Pooled TVUS and MRI sensitivities for endometrioma were 0.89 (95% confidence interval 'CI', 0.86-0.92) and 0.94 (95% CI, 0.74-0.99), respectively (indirect comparison -value of 0.47). Pooled TVUS and MRI specificities for endometrioma were 0.95 (95% CI, 0.92-0.97) and 0.94 (95% CI, 0.89-0.97), respectively (indirect comparison p-value of 0.51). These studies had a high or unclear risk of bias. A direct comparison (all participants undergoing TVUS and MRI) of the modalities was available in only two studies.
CONCLUSION
TVUS and MRI have high accuracy for diagnosing endometriomas; however, high-quality studies comparing the two modalities are lacking.
Topics: Female; Humans; Endometriosis; Prospective Studies; Ultrasonography; Magnetic Resonance Imaging; Sensitivity and Specificity; Diagnostic Tests, Routine
PubMed: 38348799
DOI: 10.1080/01443615.2024.2311664 -
European Respiratory Review : An... Dec 2022Thoracentesis and thoracoscopy are used to diagnose malignant pleural effusions (MPE). Data on how sensitivity varies with tumour type is limited. (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Thoracentesis and thoracoscopy are used to diagnose malignant pleural effusions (MPE). Data on how sensitivity varies with tumour type is limited.
METHODS
Systematic review using PubMed was performed through August 2020 to determine the sensitivity of thoracentesis and thoracoscopy for MPE secondary to malignancy, by cancer type, and complication rates. Tests to identify sources of heterogeneity were performed. Study quality was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 and National Institutes of Health quality assessment tools. Publication bias was tested using funnel plots.
RESULTS
Meta-analyses for sensitivity of thoracentesis for MPE secondary to malignancy, mesothelioma and lung and breast cancer included 29, eight, 12 and nine studies, respectively. Pooled sensitivities were 0.643 (95% CI 0.592-0.692), 0.451 (95% CI 0.249-0.661), 0.738 (95% CI 0.659-0.836) and 0.820 (95% CI 0.700-0.917), respectively. For sensitivity of thoracoscopy for MPE secondary to malignancy and mesothelioma, 41 and 15 studies were included, respectively. Pooled sensitivities were 0.929 (95% CI 0.905-0.95) and 0.915 (95% CI 0.871-0.952), respectively. Pooled complication rates of thoracentesis and thoracoscopy were 0.041 (95% CI 0.025-0.051) and 0.040 (95% CI 0.029-0.052), respectively. Heterogeneity was significant for all meta-analyses. Funnel plots were asymmetric.
INTERPRETATION
Sensitivity of thoracentesis varied significantly per cancer type. Pooled complication rates were low. Awareness of how sensitivity of thoracentesis changes across cancers can improve decision-making when MPE is suspected.
Topics: Humans; Thoracentesis; Retrospective Studies; Pleural Effusion, Malignant; Mesothelioma; Mesothelioma, Malignant; Thoracoscopy
PubMed: 36543349
DOI: 10.1183/16000617.0053-2022 -
BMC Bioinformatics Oct 2023Recent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the...
BACKGROUND
Recent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the development of de novo areas in imaging science, such as radiomics and radiogenomics, have been adding more to personalize healthcare to stratify patients better. These techniques associate imaging phenotypes with the related disease genes. Various imaging modalities have been used for years to diagnose breast cancer. Nonetheless, digital breast tomosynthesis (DBT), a state-of-the-art technique, has produced promising results comparatively. DBT, a 3D mammography, is replacing conventional 2D mammography rapidly. This technological advancement is key to AI algorithms for accurately interpreting medical images.
OBJECTIVE AND METHODS
This paper presents a comprehensive review of deep learning (DL), radiomics and radiogenomics in breast image analysis. This review focuses on DBT, its extracted synthetic mammography (SM), and full-field digital mammography (FFDM). Furthermore, this survey provides systematic knowledge about DL, radiomics, and radiogenomics for beginners and advanced-level researchers.
RESULTS
A total of 500 articles were identified, with 30 studies included as the set criteria. Parallel benchmarking of radiomics, radiogenomics, and DL models applied to the DBT images could allow clinicians and researchers alike to have greater awareness as they consider clinical deployment or development of new models. This review provides a comprehensive guide to understanding the current state of early breast cancer detection using DBT images.
CONCLUSION
Using this survey, investigators with various backgrounds can easily seek interdisciplinary science and new DL, radiomics, and radiogenomics directions towards DBT.
Topics: Humans; Female; Deep Learning; Radiographic Image Enhancement; Breast; Breast Neoplasms; Mammography
PubMed: 37884877
DOI: 10.1186/s12859-023-05515-6 -
Frontiers in Endocrinology 2023To evaluate and compare the value of conventional ultrasound-based superb microvascular imaging (SMI) and color Doppler flow imaging (CDFI) in the diagnosis of malignant... (Meta-Analysis)
Meta-Analysis
The value of conventional ultrasound combined with superb microvascular imaging and color Doppler flow imaging in the diagnosis of thyroid malignant nodules: a systematic review and meta-analysis.
PURPOSE
To evaluate and compare the value of conventional ultrasound-based superb microvascular imaging (SMI) and color Doppler flow imaging (CDFI) in the diagnosis of malignant thyroid nodule by meta-analysis.
METHODS
The literature included in the Cochrane Library, PubMed, and Embase were searched by using " superb microvascular imaging (SMI), color Doppler flow imaging (CDFI), ultrasound, thyroid nodules" as the keywords from inception through February 1, 2023. According to the inclusion and exclusion criteria, the clinical studies using SMI and CDFI to diagnose thyroid nodules were selected, and histopathology of thyroid nodules was used as reference standard. The diagnostic accuracy research quality assessment tool (QUADAS-2) was used to evaluate the quality of included literature, and the Review Manager 5.4 was used to make the quality evaluation chart. The heterogeneity test was performed on the literature that met the requirements, the combined sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were pooled, and a comprehensive ROC curve analysis was performed. Meta-DiSc version 1.4, StataSE 12, and Review Manager 5.4 software were used.
RESULTS
Finally, 13 studies were included in this meta-analysis. A total of 815 thyroid malignant nodules were assessed. All thyroid nodules were histologically confirmed after SMI or CDFI. The combined sensitivity, specificity, PLR, NLR, DOR, and area under the SROC curve of SMI for the diagnosis of malignant thyroid nodules were 0.80(95%CI: 0.77-0.83), 0.79(95%CI: 0.77-0.82), 4.37(95%CI: 3.0-6.36), 0.23(95%CI: 0.15-0.35), 22.29(95%CI: 12.18-40.78), and 0.8944, respectively; the corresponding values of CDFI were 0.62(95%CI: 0.57-0.67), 0.81(95%CI: 0.78-0.85), 3.33(95%CI: 2.18-5.07), 0.41(95%CI: 0.27-0.64), 8.93(95%CI: 3.96-20.16), and 0.8498. Deek funnel pattern showed no significant publication bias.
CONCLUSION
The diagnostic efficiency of SMI for malignant thyroid nodules is better than CDFI, and SMI technology can provide significantly more information on vascularity, make up for the deficiency of CDFI, and has better clinical application value.
SYSTEMATIC REVIEW REGISTRATION
https://www.crd.york.ac.uk/PROSPERO, identifier CRD42023402064.
Topics: Humans; Thyroid Nodule; Sensitivity and Specificity; Diagnosis, Differential; Microvessels
PubMed: 37415660
DOI: 10.3389/fendo.2023.1182259