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Journal of Cachexia, Sarcopenia and... Dec 2021Sarcopenia is defined as muscle wasting, characterized by a progressive loss of muscle mass and function due to ageing. Diagnosis of sarcopenia typically involves both...
BACKGROUND
Sarcopenia is defined as muscle wasting, characterized by a progressive loss of muscle mass and function due to ageing. Diagnosis of sarcopenia typically involves both muscle imaging and the physical performance of people exhibiting signs of muscle weakness. Despite its worldwide prevalence, a molecular method for accurately diagnosing sarcopenia has not been established.
METHODS
We develop an artificial intelligence (AI) diagnosis model of sarcopenia using a published transcriptome dataset comprising patients from multiple ethnicities. For the AI model for sarcopenia diagnosis, we use a transcriptome database comprising 17 339 genes from 118 subjects. Among the 17 339 genes, we select 27 features as the model inputs. For feature selection, we use a random forest, extreme gradient boosting and adaptive boosting. Using the top 27 features, we propose a four-layer deep neural network, named DSnet-v1, for sarcopenia diagnosis.
RESULTS
Among isolated testing datasets, DSnet-v1 provides high sensitivity (100%), specificity (94.12%), accuracy (95.83%), balanced accuracy (97.06%) and area under receiver operating characteristics (0.99). To extend the number of patient data, we develop a web application (http://sarcopeniaAI.ml/), where the model can be accessed unrestrictedly to diagnose sarcopenia if the transcriptome is available. A focused analysis of the top 27 genes for their differential or co-expression with other genes implied the potential existence of race-specific factors for sarcopenia, suggesting the possibility of identifying causal factors of sarcopenia when a more extended dataset is provided.
CONCLUSIONS
Our new AI model, DSnet-v1, accurately diagnoses sarcopenia and is currently available publicly to assist healthcare providers in diagnosing and treating sarcopenia.
Topics: Artificial Intelligence; Biomarkers; Humans; Intelligence; Prognosis; Sarcopenia
PubMed: 34704369
DOI: 10.1002/jcsm.12840 -
Journal of Clinical Pathology Nov 2021An increasing number of small pulmonary nodules are being screened by CT, and an intraoperative diagnosis is necessary for preventing excessive treatment. However, there...
AIMS
An increasing number of small pulmonary nodules are being screened by CT, and an intraoperative diagnosis is necessary for preventing excessive treatment. However, there is limited literature on the frozen diagnosis of small sclerosing pneumocytomas (SPs). In particular, tumours smaller than 1 cm are challenging for pathologists performing intraoperative frozen diagnosis.
METHODS
In total, 230 cases of SP were surgically resected between January 2015 and March 2019 at Shanghai Chest Hospital, and of them, 76 cases were smaller than 1 cm. The histology and clinical information of these 76 cases (33.0%, 76/230) were reviewed retrospectively, 54 cases of which were diagnosed intraoperatively, and the pitfalls were summarised. All diagnoses were confirmed on permanent sections and immunohistochemical sections.
RESULTS
Histologically, 78.9% (60/76) of the small SP was dominated by one growth pattern, and solid and papillary growth pattern were the most commonly misdiagnosed circumstances. The rate of intraoperative misdiagnosis of these SP smaller than 1 cm was 11.1% (6/54).
CONCLUSIONS
The main reason for misdiagnosis was failure to recognise the dual cell populations and the cellular atypia. Diagnostic clues include the gross morphology, the presence of dual-cell populations and a hypercellular papillary core, foam cell accumulation in glandular spaces and haemorrhage and haemosiderin on the periphery. In spite of awareness of pitfalls some cases may still be essentially impossible to diagnose on frozen section.
Topics: Adult; Aged; Cytodiagnosis; Diagnosis, Differential; Diagnostic Errors; Female; Frozen Sections; Humans; Intraoperative Period; Lung Neoplasms; Male; Middle Aged; Multiple Pulmonary Nodules; Retrospective Studies; Sclerosis; Sensitivity and Specificity; Solitary Pulmonary Nodule
PubMed: 33782195
DOI: 10.1136/jclinpath-2020-206729 -
Journal of the American Medical... Sep 2023Incorporating artificial intelligence (AI) into clinics brings the risk of automation bias, which potentially misleads the clinician's decision-making. The purpose of...
BACKGROUND
Incorporating artificial intelligence (AI) into clinics brings the risk of automation bias, which potentially misleads the clinician's decision-making. The purpose of this study was to propose a potential strategy to mitigate automation bias.
METHODS
This was a laboratory study with a randomized cross-over design. The diagnosis of anterior cruciate ligament (ACL) rupture, a common injury, on magnetic resonance imaging (MRI) was used as an example. Forty clinicians were invited to diagnose 200 ACLs with and without AI assistance. The AI's correcting and misleading (automation bias) effects on the clinicians' decision-making processes were analyzed. An ordinal logistic regression model was employed to predict the correcting and misleading probabilities of the AI. We further proposed an AI suppression strategy that retracted AI diagnoses with a higher misleading probability and provided AI diagnoses with a higher correcting probability.
RESULTS
The AI significantly increased clinicians' accuracy from 87.2%±13.1% to 96.4%±1.9% (P < .001). However, the clinicians' errors in the AI-assisted round were associated with automation bias, accounting for 45.5% of the total mistakes. The automation bias was found to affect clinicians of all levels of expertise. Using a logistic regression model, we identified an AI output zone with higher probability to generate misleading diagnoses. The proposed AI suppression strategy was estimated to decrease clinicians' automation bias by 41.7%.
CONCLUSION
Although AI improved clinicians' diagnostic performance, automation bias was a serious problem that should be addressed in clinical practice. The proposed AI suppression strategy is a practical method for decreasing automation bias.
Topics: Artificial Intelligence; Magnetic Resonance Imaging; Clinical Decision-Making; Humans; Anterior Cruciate Ligament Injuries; Diagnosis, Computer-Assisted
PubMed: 37561535
DOI: 10.1093/jamia/ocad118 -
Cytopathology : Official Journal of the... Sep 2023COVID-19 vaccine-associated clinical lymphadenopathy (C19-LAP) and subclinical lymphadenopathy (SLDI), which are mainly detected by 18F-FDG PET-CT, have been observed... (Review)
Review
COVID-19 vaccine-associated clinical lymphadenopathy (C19-LAP) and subclinical lymphadenopathy (SLDI), which are mainly detected by 18F-FDG PET-CT, have been observed after the introduction of RNA-based vaccines during the pandemic. Lymph node (LN) fine needle aspiration cytology (FNAC) has been used to diagnose single cases or small series of SLDI and C19-LAP. In this review, clinical and LN-FNAC features of SLDI and C19-LAP are reported and compared to non-Covid (NC)-LAP. A search for studies on C19-LAP and SLDI histopathology and cytopathology was performed on PubMed and Google Scholar, on 11 January 2023. Reports on LN-FNAC of C19-LAP were retrieved. A total of 14 reports, plus one unpublished case of C19-LAP observed in our institution, diagnosed by LN-FNAC were included in a pooled analysis and compared to the corresponding histopathological reports. In total, 26 cases were included in this review, with a mean age of 50.5 years. Twenty-one lymphadenopathies assessed by LN-FNAC were diagnosed as benign, and three cases as atypical lymphoid hyperplasia; the latter were subsequently confirmed as benign (one by repetition of LN-FNAC, two by histological control). One case of mediastinal lymphadenopathy in a patient suffering from melanoma was reported as reactive granulomatous inflammation, while one unsuspected case was diagnosed as metastasis from melanoma. In all cases, the cytological diagnoses were confirmed by follow-up or excisional biopsy. The high diagnostic value of LN-FNAC in excluding malignant processes was extremely useful in this context and may be particularly valuable when CNB or histological excisions are difficult to perform, as was the case during Covid lockdowns.
Topics: Humans; Middle Aged; Biopsy, Fine-Needle; Communicable Disease Control; COVID-19; COVID-19 Vaccines; Lymphadenopathy; Melanoma; Positron Emission Tomography Computed Tomography
PubMed: 36807950
DOI: 10.1111/cyt.13221 -
Proceedings of the National Academy of... Jan 2020Engaging in altruistic behaviors is costly, but it contributes to the health and well-being of the performer of such behaviors. The present research offers a take on how...
Engaging in altruistic behaviors is costly, but it contributes to the health and well-being of the performer of such behaviors. The present research offers a take on how this paradox can be understood. Across 2 pilot studies and 3 experiments, we showed a pain-relieving effect of performing altruistic behaviors. Acting altruistically relieved not only acutely induced physical pain among healthy adults but also chronic pain among cancer patients. Using functional MRI, we found that after individuals performed altruistic actions brain activity in the dorsal anterior cingulate cortex and bilateral insula in response to a painful shock was significantly reduced. This reduced pain-induced activation in the right insula was mediated by the neural activity in the ventral medial prefrontal cortex (VMPFC), while the activation of the VMPFC was positively correlated with the performer's experienced meaningfulness from his or her altruistic behavior. Our findings suggest that incurring personal costs to help others may buffer the performers from unpleasant conditions.
Topics: Adult; Aged; Altruism; Brain; Brain Mapping; Cerebral Cortex; Female; Gyrus Cinguli; Humans; Magnetic Resonance Imaging; Male; Middle Aged; Nervous System Physiological Phenomena; Pain; Pilot Projects; Prefrontal Cortex; Young Adult
PubMed: 31888986
DOI: 10.1073/pnas.1911861117 -
Scandinavian Journal of Medicine &... Mar 2023Psychomotor efficiency is achieved by expert performers who exhibit refined attentional strategies and efficient motor program execution. Further understanding of the...
Psychomotor efficiency is achieved by expert performers who exhibit refined attentional strategies and efficient motor program execution. Further understanding of the psychomotor efficiency hypothesis requires examination of the co-activation of key electroencephalographic (EEG) indices, including frontal theta (Fθ) power, left temporal alpha (T3α) power, the sensory-motor rhythm (SMR), and frontocentral alpha power (FCα). This study examined the relationship between these selected neural processes and the odds of successful cognitive-motor performance. EEG indices of successful and failed putts observed in twenty-seven skilled golfers were subjected to mixed-effects logistic regression analysis. The results revealed that concurrent elevations of Fθ and T3α were associated with increased odds of successful performance. The co-activation from motoric processes indicated by SMR and FCα also elevated the odds. Overall, the findings emphasize that refined attention allocation and effective motor program processing are essential cognitive features of superior cognitive-motor performance for skilled golfers.
Topics: Humans; Psychomotor Performance; Golf; Electroencephalography; Attention; Cognition; Alpha Rhythm
PubMed: 36331363
DOI: 10.1111/sms.14262 -
Pediatric Research Oct 2022Lung ultrasound (LUS) for critical patients requires trained operators to perform them, though little information exists on the level of training required for...
BACKGROUND
Lung ultrasound (LUS) for critical patients requires trained operators to perform them, though little information exists on the level of training required for independent practice. The aims were to implement a training plan for diagnosing pneumonia using LUS and to analyze the inter-observer agreement between senior radiologists (SRs) and pediatric intensive care physicians (PICPs).
METHODS
Prospective longitudinal and interventional study conducted in the Pediatric Intensive Care Unit of a tertiary hospital. Following a theoretical and practical training plan regarding diagnosing pneumonia using LUS, the concordance between SRs and the PICPs on their LUS reports was analyzed.
RESULTS
Nine PICPs were trained and tested on both theoretical and practical LUS knowledge. The mean exam mark was 13.5/15. To evaluate inter-observer agreement, a total of 483 LUS were performed. For interstitial syndrome, the global Kappa coefficient (K) was 0.51 (95% CI 0.43-0.58). Regarding the presence of consolidation, K was 0.67 (95% CI 0.53-0.78), and for the consolidation pattern, K was 0.82 (95% CI 0.79-0.85), showing almost perfect agreement.
CONCLUSIONS
Our training plan allowed PICPs to independently perform LUS and might improve pneumonia diagnosis. We found a high inter-observer agreement between PICPs and SRs in detecting the presence and type of consolidation on LUS.
IMPACT
Lung ultrasound (LUS) has been proposed as an alternative to diagnose pneumonia in children. However, the adoption of LUS in clinical practice has been slow, and it is not yet included in general clinical guidelines. The results of this study show that the implementation of a LUS training program may improve pneumonia diagnosis in critically ill patients. The training program's design, implementation, and evaluation are described. The high inter-observer agreement between LUS reports from the physicians trained and expert radiologists encourage the use of LUS not only for pneumonia diagnosis, but also for discerning bacterial and viral patterns.
Topics: Child; Humans; Prospective Studies; Pneumonia; Lung; Ultrasonography; Lung Diseases
PubMed: 34969992
DOI: 10.1038/s41390-021-01928-2 -
Journal of Voice : Official Journal of... Jul 2020To determine the consistency and accuracy of preoperative diagnosis in the voice clinic with intraoperative diagnosis and to suggest a standardized laryngeal examination... (Comparative Study)
Comparative Study
INTRODUCTION
To determine the consistency and accuracy of preoperative diagnosis in the voice clinic with intraoperative diagnosis and to suggest a standardized laryngeal examination protocol in the UK that is supported by evidence-based findings.
METHOD
From January 2011-September 2014, 164 patients were referred to the Multidisciplinary Team voice clinic and diagnosed with laryngeal pathology that required phonosurgery. The visualization (videostrobolaryngoscopy) in clinic was performed using either rigid laryngoscope or a video-naso-laryngoscope. Intraoperatively, laryngeal visualization and surgical procedure was conducted using Storz Aida HD system, 10-mm rigid laryngoscope 0° or 5-mm rigid laryngoscope 0°/30° and a Zeiss S7 microscope.
RESULTS
Of the 164 patients seen in the multidisciplinary voice clinic, 86 clinic diagnoses were confirmed intraoperatively (52.4%), 15 patients had the diagnosis confirmed intraoperatively with additional lesion found (9.1%). The clinic diagnosis changed intraoperatively in 63 cases (38.4%). 61 (37.2%) patients seen in the voice clinic were diagnosed with cyst, in 39.3% the diagnosis was confirmed intraoperatively with 5 cases (8.2%) having an additional diagnosis. Twenty (12.2%) patients were diagnosed with polyps, with 80% confirmation intraoperatively; 3 patients (10%) had an additional diagnosis.
CONCLUSION
Videolaryngostroboscopy imaging of the larynx provides an outpatient tool for accurately diagnosing more than 50% of laryngeal pathologies when interpreted by multidisciplinary voice clinicians. However direct laryngeal examination under general anesthesia remains the gold standard when obtaining accurate diagnoses of laryngeal pathology. Patients diagnosed with nonorganic voice disorders should be considered for direct laryngoscopy under general anesthetic should they fail to respond to conservative management.
Topics: Adolescent; Adult; Aged; Aged, 80 and over; Ambulatory Care; Child; Child, Preschool; Clinical Decision-Making; Diagnostic Errors; Female; Humans; Intraoperative Care; Laryngeal Diseases; Laryngoscopes; Laryngoscopy; London; Male; Middle Aged; Observer Variation; Operating Rooms; Predictive Value of Tests; Reproducibility of Results; Stroboscopy; Voice Disorders; Young Adult
PubMed: 30660339
DOI: 10.1016/j.jvoice.2018.12.016 -
Journal of Magnetic Resonance Imaging :... Aug 2022The diagnosis of labral injury on MRI is time-consuming and potential for incorrect diagnoses. (Randomized Controlled Trial)
Randomized Controlled Trial
BACKGROUND
The diagnosis of labral injury on MRI is time-consuming and potential for incorrect diagnoses.
PURPOSE
To explore the feasibility of applying deep learning to diagnose and classify labral injuries with MRI.
STUDY TYPE
Retrospective.
POPULATION
A total of 1016 patients were divided into normal (n = 168, class 0) and abnormal labrum (n = 848) groups. The abnormal group consisted of n = 111 with class 1 (degeneration), n = 437 with class 2 (partial or complete tear), and n = 300 with unclassified injury. Patients were randomly divided into training, validation, and test cohort according to the ratio of 55%:15%:30%.
FIELD STRENGTH/SEQUENCE
Fat-saturation proton density-weighted fast spin-echo sequence at 3.0 T.
ASSESSMENT
Convolutional neural network-6 (CNN-6) was used to extract, discriminate, and detect oblique coronal (OCOR) and oblique sagittal (OSAG) images. Mask R-CNN was used for segmentation. LeNet-5 was used to diagnose and classify labral injuries. The weighting method combined the models of OCOR and OSAG. The output-input connection was used to correlate the whole diagnosis/classification system. Four radiologists performed subjective diagnoses to obtain the diagnosis results.
STATISTICAL TESTS
CNN-6 and LeNet-5 were evaluated by area under the receiver operating characteristic (ROC) curve and related parameters. The mean average precision (MAP) evaluated the Mask R-CNN. McNemar's test was used to compare the radiologists and models. A P value < 0.05 was considered statistically significant.
RESULTS
The area under the curve (AUC) of CNN-6 was 0.99 for extraction, discrimination, and detection. MAP values of Mask R-CNN for OCOR and OSAG image segmentation were 0.96 and 0.99. The accuracies of LeNet-5 in the diagnosis and classification were 0.94/0.94 (OCOR) and 0.92/0.91 (OSAG), respectively. The accuracy of the weighted models in the diagnosis and classification were 0.94 and 0.97, respectively. The accuracies of radiologists in the diagnosis and classification of labrum injuries ranged from 0.85 to 0.92 and 0.78 to 0.94, respectively.
DATA CONCLUSION
Deep learning can assist radiologists in diagnosing and classifying labrum injuries.
EVIDENCE LEVEL
3 TECHNICAL EFFICACY: Stage 2.
Topics: Deep Learning; Hip Joint; Humans; Magnetic Resonance Imaging; Neural Networks, Computer; Retrospective Studies
PubMed: 35081273
DOI: 10.1002/jmri.28069 -
Computer Methods and Programs in... Oct 2022Nowadays, COVID-19 is spreading rapidly worldwide, and seriously threatening lives . From the perspective of security and economy, the effective control of COVID-19 has...
OBJECTIVE
Nowadays, COVID-19 is spreading rapidly worldwide, and seriously threatening lives . From the perspective of security and economy, the effective control of COVID-19 has a profound impact on the entire society. An effective strategy is to diagnose earlier to prevent the spread of the disease and prompt treatment of severe cases to improve the chance of survival.
METHODS
The method of this paper is as follows: Firstly, the collected data set is processed by chest film image processing, and the bone removal process is carried out in the rib subtraction module. Then, the set preprocessing method performed histogram equalization, sharpening, and other preprocessing operations on the chest film. Finally, shallow and high-level feature mapping through the backbone network extracts the processed chest radiographs. We implement the self-attention mechanism in Inception-Resnet, perform the standard classification, and identify chest radiograph diseases through the classifier to realize the auxiliary COVID-19 diagnosis process at the medical level, all in an effort to further enhance the classification performance of the convolutional neural network. Numerous computer simulations demonstrate that the Inception-Resnet convolutional neural network performs CT image categorization and enhancement with greater efficiency and flexibility than conventional segmentation techniques.
RESULTS
The experimental COVID-19 CT dataset obtained in this paper is the new data for CT scans and medical imaging of normal, early COVID-19 patients and severe COVID-19 patients from Jinyintan hospital. The experiment plots the relationship between model accuracy, model loss and epoch, using ACC, TPR, SPE, F1 score and G-mean to measure the image maps of patients with and without the disease. Statistical measurement values are obtained by Inception-Resnet are 88.23%, 83.45%, 89.72%, 95.53% and 88.74%. The experimental results show that Inception-Resnet plays a more effective role than other image classification methods in evaluation indicators, and the method has higher robustness, accuracy and intuitiveness.
CONCLUSION
With CT images in the clinical diagnosis of COVID-19 images being widely used and the number of applied samples continuously increasing, the method in this paper is expected to become an additional diagnostic tool that can effectively improve the diagnostic accuracy of clinical COVID-19 images.
Topics: COVID-19; COVID-19 Testing; Humans; Image Processing, Computer-Assisted; Lung; Neural Networks, Computer
PubMed: 35964421
DOI: 10.1016/j.cmpb.2022.107053