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Diagnostic Accuracy of SPECT for Mild Traumatic Brain Injury: A Systematic Review and Meta-analysis.Clinical Nuclear Medicine Jun 2024This study examines the diagnostic accuracy of brain perfusion SPECT for mild traumatic brain injury (mTBI).
PURPOSE
This study examines the diagnostic accuracy of brain perfusion SPECT for mild traumatic brain injury (mTBI).
PATIENTS AND METHODS
A systematic review and meta-analysis was performed according to PRISMA guidelines (PROSPERO: CRD42023484636). Five databases were searched for studies evaluating brain perfusion SPECT in adult patients with mTBI (GCS 13-15). Study quality was assessed using a modified QUADAS-2 tool. A meta-analysis was performed to pool proportions of hypoperfusion abnormalities across brain lobes.
RESULTS
Of 4735 records, 22 studies (5 longitudinal [40% high quality], 17 cross-sectional [24% high quality]) were included totaling 800 patients (mean age, 37.4 ± 12.6 years; 36.4% female). Meta-analysis of proportions indicated that the frontal lobe most frequently showed hypoperfusion on brain perfusion SPECT (pooled proportion 40.1% [95% confidence interval, 31.2% to 49.8%], 99/254, I2 = 54.5%), followed by the temporal lobe (26.1% [95% confidence interval, 19.9% to 33.6%], 68/254, I2 = 30.7%). Several studies found that hypoperfusion abnormalities were associated with neuropsychological findings. Also, brain perfusion SPECT could detect abnormalities not seen on MRI. Abnormalities in perfusion on brain perfusion SPECT may be more readily detected with a quantitative assessment compared with a visual assessment alone, although there appears to be no consensus on the optimal method for image interpretation. Evidence evaluating the sensitivity and specificity of brain perfusion SPECT for mTBI was limited. Using the GRADE framework, the evidence was rated as low.
CONCLUSIONS
Although perfusion abnormalities can be seen in patients with mTBI, commonly in the frontal and temporal lobes, the findings are nonspecific and may derive from various factors. Ultimately, brain perfusion SPECT provides additional information for mTBI, but the final added value for the detection of mTBI is unknown.
PubMed: 38914012
DOI: 10.1097/RLU.0000000000005328 -
The Indian Journal of Radiology &... Jul 2024Both computed tomography (CT) and magnetic resonance imaging (MRI) play significant roles in assessing patients with dizziness. However, understanding the... (Review)
Review
Comparative Diagnostic Accuracy of Computed Tomography Scan versus Magnetic Resonance Imaging in the Emergency Department for the Evaluation of Dizziness: A Systematic Review.
Both computed tomography (CT) and magnetic resonance imaging (MRI) play significant roles in assessing patients with dizziness. However, understanding the comparative capabilities of these imaging methods in detecting pathological causes is crucial for determining the most suitable modality. This review aims to evaluate the diagnostic accuracy and clinical utility of MRI and CT scans in managing patients with acute dizziness in the emergency department. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a comprehensive search in various databases (PubMed, Google Scholar, Cochrane library, British Medical Journals, and ScienceDirect) from 2010 to 2023. We used the QUADAS-2 tool to assess bias risk, considering MRI as the reference standard and CT scan as the index test. The final analysis included six studies, with 3,993 patients (48% male, 52% female; average age: 56.7 years). Three studies were of high quality, two of medium quality, and one of low quality. Central ischemia was the predominant diagnosis for dizziness. MRI demonstrated higher diagnostic efficacy for stroke compared with CT scans, while mixed results were observed for other multiple diseases when both MRI and CT scans were used. MRI outperforms CT scans in diagnosing dizziness-related strokes. However, for other causes of dizziness, there is no significant difference between these techniques. Nevertheless, it is crucial to acknowledge the limitations associated with MRI. Consequently, to address these concerns, the selection of an imaging technique should be tailored to the individual based on factors such as their clinical presentation, comorbidities, and socioeconomic circumstances.
PubMed: 38912244
DOI: 10.1055/s-0044-1778726 -
The Indian Journal of Radiology &... Jul 2024Although abundant literature is currently available on the use of deep learning for breast cancer detection in mammography, the quality of such literature is widely... (Review)
Review
Although abundant literature is currently available on the use of deep learning for breast cancer detection in mammography, the quality of such literature is widely variable. To evaluate published literature on breast cancer detection in mammography for reproducibility and to ascertain best practices for model design. The PubMed and Scopus databases were searched to identify records that described the use of deep learning to detect lesions or classify images into cancer or noncancer. A modification of Quality Assessment of Diagnostic Accuracy Studies (mQUADAS-2) tool was developed for this review and was applied to the included studies. Results of reported studies (area under curve [AUC] of receiver operator curve [ROC] curve, sensitivity, specificity) were recorded. A total of 12,123 records were screened, of which 107 fit the inclusion criteria. Training and test datasets, key idea behind model architecture, and results were recorded for these studies. Based on mQUADAS-2 assessment, 103 studies had high risk of bias due to nonrepresentative patient selection. Four studies were of adequate quality, of which three trained their own model, and one used a commercial network. Ensemble models were used in two of these. Common strategies used for model training included patch classifiers, image classification networks (ResNet in 67%), and object detection networks (RetinaNet in 67%). The highest reported AUC was 0.927 ± 0.008 on a screening dataset, while it reached 0.945 (0.919-0.968) on an enriched subset. Higher values of AUC (0.955) and specificity (98.5%) were reached when combined radiologist and Artificial Intelligence readings were used than either of them alone. None of the studies provided explainability beyond localization accuracy. None of the studies have studied interaction between AI and radiologist in a real world setting. While deep learning holds much promise in mammography interpretation, evaluation in a reproducible clinical setting and explainable networks are the need of the hour.
PubMed: 38912238
DOI: 10.1055/s-0043-1775737 -
Current Status and Role of Artificial Intelligence in Anorectal Diseases and Pelvic Floor Disorders.JSLS : Journal of the Society of... 2024Anorectal diseases and pelvic floor disorders are prevalent among the general population. Patients may present with overlapping symptoms, delaying diagnosis, and... (Review)
Review
BACKGROUND
Anorectal diseases and pelvic floor disorders are prevalent among the general population. Patients may present with overlapping symptoms, delaying diagnosis, and lowering quality of life. Treating physicians encounter numerous challenges attributed to the complex nature of pelvic anatomy, limitations of diagnostic techniques, and lack of available resources. This article is an overview of the current state of artificial intelligence (AI) in tackling the difficulties of managing benign anorectal disorders and pelvic floor disorders.
METHODS
A systematic literature review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched the PubMed database to identify all potentially relevant studies published from January 2000 to August 2023. Search queries were built using the following terms: AI, machine learning, deep learning, benign anorectal disease, pelvic floor disorder, fecal incontinence, obstructive defecation, anal fistula, rectal prolapse, and anorectal manometry. Malignant anorectal articles and abstracts were excluded. Data from selected articles were analyzed.
RESULTS
139 articles were found, 15 of which met our inclusion and exclusion criteria. The most common AI module was convolutional neural network. researchers were able to develop AI modules to optimize imaging studies for pelvis, fistula, and abscess anatomy, facilitated anorectal manometry interpretation, and improved high-definition anoscope use. None of the modules were validated in an external cohort.
CONCLUSION
There is potential for AI to enhance the management of pelvic floor and benign anorectal diseases. Ongoing research necessitates the use of multidisciplinary approaches and collaboration between physicians and AI programmers to tackle pressing challenges.
Topics: Humans; Pelvic Floor Disorders; Artificial Intelligence; Rectal Diseases; Anus Diseases; Manometry; Fecal Incontinence
PubMed: 38910957
DOI: 10.4293/JSLS.2024.00007 -
Cureus May 2024Neuroendocrine tumors (NETs) represent a heterogeneous group of neoplasms with diverse clinical presentations and prognoses. Accurate and timely diagnosis of these... (Review)
Review
Neuroendocrine tumors (NETs) represent a heterogeneous group of neoplasms with diverse clinical presentations and prognoses. Accurate and timely diagnosis of these tumors is crucial for appropriate management and improved patient outcomes. In recent years, exciting advancements in artificial intelligence (AI) technologies have been revolutionizing medical diagnostics, particularly in the realm of detecting and characterizing pulmonary NETs, offering promising avenues for improved patient care. This article aims to provide a comprehensive overview of the role of AI in diagnosing lung NETs. We discuss the current challenges associated with conventional diagnostic approaches, including histopathological examination and imaging modalities. Despite advancements in these techniques, accurate diagnosis remains challenging due to the overlapping features with other pulmonary lesions and the subjective interpretation of imaging findings. AI-based approaches, including machine learning and deep learning algorithms, have demonstrated remarkable potential in addressing these challenges. By leveraging large datasets of radiological images, histopathological samples, and clinical data, AI models can extract complex patterns and features that may not be readily discernible to human observers. Moreover, AI algorithms can continuously learn and improve from new data, leading to enhanced diagnostic accuracy and efficiency over time. Specific AI applications in the diagnosis of lung NETs include computer-aided detection and classification of pulmonary nodules on CT scans, quantitative analysis of PET imaging for tumor characterization, and integration of multi-modal data for comprehensive diagnostic assessments. These AI-driven tools hold promise for facilitating early detection, risk stratification, and personalized treatment planning in patients with lung NETs.
PubMed: 38910787
DOI: 10.7759/cureus.61012 -
Cureus May 2024Diagnosing endometrial carcinoma correctly is essential for appropriate treatment, as it is a major health risk. As machine learning (ML) and artificial intelligence... (Review)
Review
Diagnosing endometrial carcinoma correctly is essential for appropriate treatment, as it is a major health risk. As machine learning (ML) and artificial intelligence (AI) have grown in popularity, so has interest in their potential to improve cancer diagnosis accuracy. In the context of endometrial cancer, this study attempts to examine the efficacy as well as the accuracy of AI-assisted diagnostic approaches. Additionally, it aims to methodically evaluate the contribution of AI and ML techniques to the improvement of endometrial cancer diagnosis. Following PRISMA guidelines, we performed a thorough search of numerous databases, including Medline via Ovid, PubMed, Scopus, Web of Science, and Google Scholar. Ten years were searched, encompassing both basic and advanced research. Peer-reviewed papers and original research studies that explicitly looked at the application of AI/ML in endometrial cancer diagnosis were the main targets of the well-defined selection criteria. Using the Critical Appraisal Skills Programme (CASP) methodology, two independent researchers conducted a thorough screening process and quality assessment of included studies. The review found a notable inclination towards the effective use of AI in endometrial carcinoma diagnostics, namely in the identification and categorization of endometrial cancer. Artificial intelligence models, particularly Convolutional Neural Networks (CNNs) and deep learning algorithms have shown remarkable precision in detecting endometrial cancer. They frequently achieve or even exceed the diagnostic proficiency of human specialists. The use of artificial intelligence in medical diagnostics signifies revolutionary progress in the field of oncology. AI-assisted diagnostic tools have demonstrated the potential to improve the precision and effectiveness of cancer diagnosis, namely in cases of endometrial carcinoma. This innovation not only enhances the quality of patient care but also indicates a transition towards more individualized and efficient treatment approaches in the field of oncology. The advancement of AI technology is expected to play a crucial role in medical diagnostics, particularly in the field of cancer detection and treatment, perhaps leading to a significant transformation in the approach to these areas.
PubMed: 38910646
DOI: 10.7759/cureus.60973 -
Current Cardiology Reviews Jun 2024Internet Gaming Disorder (IGD) is recognized as a mental health condition associated with excessive video gaming, leading to functional impairments. The inclusion of IGD...
BACKGROUND
Internet Gaming Disorder (IGD) is recognized as a mental health condition associated with excessive video gaming, leading to functional impairments. The inclusion of IGD in the DSM-5 has underscored the importance of comprehensively understanding its physiological and psychological effects.
OBJECTIVE
This systematic review aims to analyze and synthesize existing literature on the cardiophysiological and neurophysiological activities of individuals diagnosed with IGD, with a focus on identifying patterns, trends, and implications for clinical practice and future research.
METHODS
A systematic search was conducted in PubMed and Scopus databases to identify relevant studies published up to 2023. The search strategy included terms related to IGD, cardiophysiology, neurophysiology, and relevant measurement techniques. Inclusion criteria encompassed peer-reviewed research articles and clinical trials examining cardiophysiological (e.g., heart rate variability, blood pressure) and neurophysiological (e.g., brain imaging, electroencephalography) parameters in individuals with IGD. Exclusion criteria were applied to ensure methodological rigor and relevance to the research question.
RESULTS
The initial search yielded 1320 papers related to IGD, of which twenty studies met the eligibility criteria and were included in the review. Data extraction and synthesis focused on key cardiophysiological and neurophysiological outcomes observed in individuals with IGD compared to healthy controls. Findings revealed decreased Heart Rate Variability (HRV), increased sympathetic activity, and executive control deficits in IGD individuals based on Electrocardiogram (ECG) recordings and cognitive assessments. Neuroimaging studies demonstrated heightened brain activation in the lateral and prefrontal cortex, altered reward processing, and impulse control mechanisms among IGD subjects. Gender-specific differences were noted, with males exhibiting distinct thalamic activation striatum and decreased Regional Homogeneity (ReHo) in the right Posterior Cingulate (rPCC) compared to females.
DISCUSSION
The synthesized evidence indicates a complex interplay between excessive gaming and cardiophysiological/neurophysiological changes, highlighting the need for multidimensional assessments in diagnosing and managing IGD. Implications for clinical practice include early detection using ECG, EEG, and advanced neuroimaging techniques, as well as personalized interventions tailored to individual characteristics and gender-specific differences.
CONCLUSION
This systematic review provides a comprehensive overview of the cardiophysiological and neurophysiological activities associated with Internet Gaming Disorder. The findings underscore the need for further research to elucidate underlying mechanisms, develop standardized diagnostic protocols, and optimize targeted interventions for individuals with IGD.
PubMed: 38910426
DOI: 10.2174/011573403X295560240530104352 -
Journal of Orthopaedic Surgery and... Jun 2024Compartment syndrome is a well-known phenomenon that is most commonly reported in the extremities. However, paralumbar compartment syndrome is rarely described in... (Review)
Review
BACKGROUND
Compartment syndrome is a well-known phenomenon that is most commonly reported in the extremities. However, paralumbar compartment syndrome is rarely described in available literature. The authors present a case of paralumbar compartment syndrome after high intensity deadlifting.
CASE PRESENTATION
53-year-old male who presented with progressively worsening low back pain and paresthesias one day after high-intensity deadlifting. Laboratory testing found the patient to be in rhabdomyolysis; he was admitted for intravenous fluid resuscitation and pain control. Orthopedics was consulted, and Magnetic Resonance Imaging revealed significant paravertebral edema and loss of muscle striation. Given the patient's lack of improvement with intravenous and oral pain control, clinical and radiographic findings, there was significant concern for acute paralumbar compartment syndrome. The patient subsequently underwent urgent fasciotomy of bilateral paralumbar musculature with delayed closure.
CONCLUSION
Given the paucity of literature on paralumbar compartment syndrome, the authors' goal is to promote awareness of the diagnosis, as it should be included in the differential diagnosis of intractable back pain after high exertional exercise. The current literature suggests that operative cases of paralumbar compartment syndromes have a higher rate of return to pre-operative function compared to those treated non-operatively. This case report further supports this notion. The authors recommend further study into this phenomenon, given its potential to result in persistent chronic exertional pain and irreversible tissue damage.
Topics: Humans; Male; Middle Aged; Compartment Syndromes; Low Back Pain; Rhabdomyolysis; Lifting
PubMed: 38909253
DOI: 10.1186/s13018-024-04860-3 -
Hand (New York, N.Y.) Jun 2024The purpose of this systematic review is to describe the pathoanatomy, presentation, diagnostic workup, treatment modalities, and outcomes of posterior interosseous... (Review)
Review
The purpose of this systematic review is to describe the pathoanatomy, presentation, diagnostic workup, treatment modalities, and outcomes of posterior interosseous nerve (PIN) palsy in patients with rheumatoid arthritis (RA). All reported cases of PIN palsy in patients with RA were reviewed to yield 72 cases of PIN palsy in 70 patients. The male-to-female ratio was 1:2.7. Pain involving the elbow was very common (20/33 cases reporting this information), and paralysis or weakness of digit extension was noted in 27/33 cases and 6/33 cases, respectively. Only 1 of the 54 cases undergoing surgical intervention reported persistent weakness, and this 1 patient had undergone a 3-month trial of conservative management. In conclusion, Appropriate pharmacologic management in conjunction with magnetic resonance imaging (MRI) and ultrasound monitoring may be used for conservative management, but surgical decompression should still be utilized for patients with a compressive disease pathology who fail to improve with 6 weeks of conservative treatment, or for those with advanced disease on initial presentation.
PubMed: 38907655
DOI: 10.1177/15589447241260766 -
BMC Cardiovascular Disorders Jun 2024Transcatheter aortic valve implantation (TAVI) is a well-established treatment for high and intermediate-risk patients with severe aortic stenosis (AS). Recent studies...
BACKGROUND
Transcatheter aortic valve implantation (TAVI) is a well-established treatment for high and intermediate-risk patients with severe aortic stenosis (AS). Recent studies have demonstrated non-inferiority of TAVI compared to surgery in low-risk patients. In the past decade, numerous literature reviews (SLRs) have assessed the use of TAVI in different risk groups. This is the first attempt to provide an overview of SRs (OoSRs) focusing on secondary studies reporting clinical outcomes/process indicators. This research aims to summarize the findings of extant literature on the performance of TAVI over time.
METHODS
A literature search took place from inception to April 2024. We searched MEDLINE and the Cochrane Library for SLRs. SLRs reporting at least one review of clinical indicators were included. Subsequently, a two-step inclusion process was conducted: [1] screening based on title and abstracts and [2] screening based on full-text papers. Relevant data were extracted and the quality of the reviews was assessed.
RESULTS
We included 33 SLRs with different risks assessed via the Society of Thoracic Surgeons (STS) score. Mortality rates were comparable between TAVI and Surgical Aortic Valve Replacement (SAVR) groups. TAVI is associated with lower rates of major bleeding, acute kidney injury (AKI) incidence, and new-onset atrial fibrillation. Vascular complications, pacemaker implantation, and residual aortic regurgitation were more frequent in TAVI patients.
CONCLUSION
This study summarizes TAVI performance findings over a decade, revealing a shift to include both high and low-risk patients since 2020. Overall, TAVI continues to evolve, emphasizing improved outcomes, broader indications, and addressing challenges.
Topics: Humans; Transcatheter Aortic Valve Replacement; Aortic Valve Stenosis; Risk Factors; Treatment Outcome; Risk Assessment; Aortic Valve; Postoperative Complications; Time Factors; Systematic Reviews as Topic
PubMed: 38907344
DOI: 10.1186/s12872-024-03980-2