با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
1 |
Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery
بینایی عمیق مبتنی بر یادگیری برای تشخیص و طبقه بندی حرکات بخیه در جراحی با کمک روبات-2021 Background: Our previous work classified a taxonomy of suturing gestures during a vesicourethral
anastomosis of robotic radical prostatectomy in association with tissue tears and patient outcomes.
Herein, we train deep learning-based computer vision to automate the identification and classification of
suturing gestures for needle driving attempts.
Methods: Using two independent raters, we manually annotated live suturing video clips to label timepoints and gestures. Identification (2,395 videos) and classification (511 videos) datasets were compiled to train computer vision models to produce 2- and 5-class label predictions, respectively. Networks were trained on inputs of raw red/blue/green pixels as well as optical flow for each frame. Each model was trained on 80/20 train/test splits. Results: In this study, all models were able to reliably predict either the presence of a gesture (identification, area under the curve: 0.88) as well as the type of gesture (classification, area under the curve: 0.87) at significantly above chance levels. For both gesture identification and classification datasets, we observed no effect of recurrent classification model choice (long short-term memory unit versus convolutional long short-term memory unit) on performance. Conclusion: Our results demonstrate computer vision’s ability to recognize features that not only can identify the action of suturing but also distinguish between different classifications of suturing gestures. This demonstrates the potential to utilize deep learning computer vision toward future automation of surgical skill assessment. |
مقاله انگلیسی |
2 |
Automated Vision-Based Microsurgical Skill Analysis in Neurosurgery Using Deep Learning: Development and Preclinical Validation
تجزیه و تحلیل خودکار مهارتهای میکروجراحی مبتنی بر بینایی در جراحی مغز و اعصاب با استفاده از یادگیری عمیق: توسعه و اعتبار پیش بالینی-2021 - BACKGROUND/OBJECTIVE: Technical skill acquisition
is an essential component of neurosurgical training.
Educational theory suggests that optimal learning and
improvement in performance depends on the provision of
objective feedback. Therefore, the aim of this study was to
develop a vision-based framework based on a novel representation of surgical tool motion and interactions
capable of automated and objective assessment of microsurgical skill.
- METHODS: Videos were obtained from 1 expert, 6 intermediate, and 12 novice surgeons performing arachnoid dissection in a validated clinical model using a standard operating microscope. A mask region convolutional neural network framework was used to segment the tools present within the operative field in a recorded video frame. Tool motion analysis was achieved using novel triangulation metrics. Performance of the framework in classifying skill levels was evaluated using the area under the curve and accuracy. Objective measures of classifying the surgeons skill level were also compared using the ManneWhitney U test, and a value of P < 0.05 was considered statistically significant. - RESULTS: The area under the curve was 0.977 and the accuracy was 84.21%. A number of differences were found, which included experts having a lower median dissector velocity (P [ 0.0004; 190.38 mse1 vs. 116.38 mse1), and a smaller inter-tool tip distance (median 46.78 vs. 75.92; P [ 0.0002) compared with novices. - CONCLUSIONS: Automated and objective analysis of microsurgery is feasible using a mask region convolutional neural network, and a novel tool motion and interaction representation. This may support technical skills training and assessment in neurosurgery. Key words: Artificial intelligence | Computer vision | Convolutional neural network | Mask RCNN | Microsurgery | Motion-analysis | Neurosurgery |
مقاله انگلیسی |
3 |
MRI-based traditional radiomics and computer-vision nomogram for predicting lymphovascular space invasion in endometrial carcinoma
رادیومیک سنتی مبتنی بر MRI و نام نگاری رایانه ای برای پیش بینی تهاجم فضایی لنفاوی در سرطان آندومتر-2021 Purpose: To determine the capabilities of MRI-based traditional radiomics and computer-vision (CV)
nomogram for predicting lymphovascular space invasion (LVSI) in patients with endometrial carcinoma
(EC).
Materials and methods: A total of 184 women (mean age, 52.9 ± 9.0 [SD] years; range, 28–82 years) with EC were retrospectively included. Traditional radiomics features and CV features were extracted from preoperative T2-weighted and dynamic contrast-enhanced MR images. Two models (Model 1, the radiomics model; Model 2, adding CV radiomics signature into the Model 1) were built. The performance of the models was evaluated by the area under the curve (AUC) of the receiver operator characteristic (ROC) in the training and test cohorts. A nomogram based on clinicopathological metrics and radiomics signatures was developed. The predictive performance of the nomogram was assessed by AUC of the ROC in the training and test cohorts. Results: For predicting LVSI, the AUC values of Model 1 in the training and test cohorts were 0.79 (95% confidence interval [CI]: 0.702–0.889; accuracy: 65.9%; sensitivity: 88.8%; specificity: 57.8%) and 0.75 (95% CI: 0.585–0.914; accuracy: 69.5%; sensitivity: 85.7%; specificity: 62.5%), respectively. The AUC values of Model 2 in the training and test cohorts were 0.93 (95% CI: 0.875–0.991; accuracy: 94.9%; sensitivity: 91.6%; specificity: 96.0%) and 0.81 (95% CI: 0.666–0.962; accuracy: 71.7%; sensitivity: 92.8%; specificity: 62.5%), respectively. The discriminative ability of Model 2 was significantly improved compared to Model 1 (Net Reclassification Improvement [NRI] = 0.21; P = 0.04). Based on histologic grade, FIGO stage, Radscore and CV-score, AUC values of the nomogram to predict LVSI in the training and test cohorts were 0.98 (95% CI: 0.955–1; accuracy: 91.6%; sensitivity: 91.6%; specificity: 96.0%) and 0.92 (95% CI: 0.823–1; accuracy: 91.3%; sensitivity: 78.5%; specificity: 96.8%), respectively. Conclusions: MRI-based traditional radiomics and computer-vision nomogram are useful for preoperative risk stratification in patients with EC and may facilitate better clinical decision-making. Keywords: Uterus | Endometrial neoplasm | Magnetic resonance imaging | Nomogram | Computer vision |
مقاله انگلیسی |
4 |
Identifying influential factors distinguishing recidivists among offender patients with a diagnosis of schizophrenia via machine learning algorithms
شناسایی عوامل موثر در تشخیص تکرار مجدد در بین بیماران مجرم با تشخیص اسکیزوفرنی از طریق الگوریتم های یادگیری ماشین-2020 Purpose: There is a lack of research on predictors of criminal recidivism of offender patients diagnosed
with schizophrenia.
Methods: 653 potential predictor variables were anlyzed in a set of 344 offender patients with a diagnosis
of schizophrenia (209 reconvicted) using machine learning algorithms. As a novel methodological
approach, null hypothesis significance testing (NHST), backward selection, logistic regression, trees,
support vector machines (SVM), and naive bayes were used for preselecting variables. Subsequently the
variables identified as most influential were used for machine learning algorithm building and
evaluation.
Results: The two final models (with/without imputation) predicted criminal recidivism with an accuracy
of 81.7 % and 70.6 % and a predictive power (area under the curve, AUC) of 0.89 and 0.76 based on the
following predictors: prescription of amisulpride prior to reoffending, suspended sentencing to
imprisonment, legal complaints
filed by relatives/therapists/public authorities, recent legal issues, number of offences leading to forensic treatment, anxiety upon discharge, being single, violence toward care team and constant breaking of rules during treatment, illegal opioid use, middle east as place of
birth, and time span since the last psychiatric inpatient treatment.
Conclusion: Results provide new insight on possible factors influencing persistent offending in a specific
subgroup of patients with a schizophrenic spectrum disorder. Keywords: Criminal justice | Criminal recidivism | Machine learning | Offender | Schizophrenia |
مقاله انگلیسی |
5 |
AI-based detection of erythema migrans and disambiguation against other skin lesions
تشخیص اریتم مهاجر بر اساس هوش مصنوعی و ابهام زدایی در برابر سایر ضایعات پوستی-2020 This study examines the use of AI methods and deep learning (DL) for prescreening skin lesions
and detecting the characteristic erythema migrans rash of acute Lyme disease. Accurate
identification of erythema migrans allows for early diagnosis and treatment, which avoids the
potential for later neurologic, rheumatologic, and cardiac complications of Lyme disease. We
develop and test several deep learning models for detecting erythema migrans versus several
other clinically relevant skin conditions, including cellulitis, tinea corporis, herpes zoster,
erythema multiforme, lesions due to tick bites and insect bites, as well as non-pathogenic
normal skin. We consider a set of clinically-relevant binary and multiclass classification
problems of increasing complexity. We train the DL models on a combination of publicly
available images and test on public as well as images obtained in the clinical setting. We report
performance metrics that measure agreement with a gold standard, as well as a receiver
operating characteristic curve and associated area under the curve. On public images, we find
that the DL system has an accuracy ranging from 71.58% (and 95% error margin equal to 3.77%)
for an 8-class problem of EM versus 7 other classes including other skin pathologies, insect bites
and normal skin, to 94.23% (3.66%) for a binary problem of EM vs. non-pathological skin. On
clinical images of affected individuals, the DL system has a sensitivity of 88.55% (2.39%). These
results suggest that a DL system can help in prescreening and referring individuals to physicians
for earlier diagnosis and treatment, in the presence of clinically relevant confusers, thereby
reducing further complications and morbidity. |
مقاله انگلیسی |
6 |
The role of cognitive functioning in predicting restoration among criminal defendants committed for inpatient restoration of competence to stand trial
نقش عملکرد شناختی در پیش بینی ترمیم در بین متهمان جنایی مرتکب برای اعاده صلاحیت بستری برای محاکمه-2020 In the United States, the due process and equal protection clauses of the 14th Amendment require that criminal defendants found incompetent to stand trial be committed for competency restoration only for such a time considered to be reasonable to achieve this aim. Adherence to these protections requires that forensic clinicians have the capacity to accurately identify defendants unlikely to be restored and to provide evidence-based estimates regarding anticipated restoration timelines. The present study examines restoration rates in a large sample (N = 492) of incompetent male defendants consecutively admitted for inpatient competency restoration between 2013 and 2017. Expanding on prior research suggesting that shared cognitive deficits might underlie impaired competency-related abilities across diverse psychiatric illnesses, the predictive classification accuracy of the Mini Mental State Examination (MMSE) on restoration was examined. Results showed that 90.4% of all defendants were restored in an average of 90.5 days and that restoration rates differed across psychiatric classification, such that patients with mood and psychotic disorders were more likely to be restored whereas those with intellectual disabilities and neurocognitive disorders were less likely. The MMSE was associated with restoration outcomes, such that over 90% of patients with no or mild cognitive impairment were restored compared to 68% of patients with severe cognitive impairment. Multivariable logistic regression showed that each one-point decrease on the MMSE total score was associated with a 19.7% (p < .001)increased odds of restoration failure and the MMSE total score produced an area under the curve (AUC) of 0.767. The MMSE appears to provide a brief, reliable screening instrument to quantify the presence and severity of cognitive impairment underlying a range of serious psychiatric illness that is capable of discriminating defendants based upon their likelihood of being restored in a reasonable amount of time with a moderate degree of accuracy. |
مقاله انگلیسی |
7 |
Using artificial intelligence (AI) to predict postoperative surgical site infection: A retrospective cohort of 4046 posterior spinal fusions
استفاده از هوش مصنوعی برای پیش بینی عفونت محل جراحی بعد از عمل: یک گروه گذشته نگر از 4046 اتصالات خلفی ستون فقرات-2020 Objectives: Machine Learning and Artificial Intelligence (AI) are rapidly growing in capability and increasingly
applied to model outcomes and complications within medicine. In spinal surgery, post-operative surgical site
infections (SSIs) are a rare, yet morbid complication. This paper applied AI to predict SSIs after posterior spinal
fusions.
Patients and Methods: 4046 posterior spinal fusions were identified at a single academic center. A Deep Neural
Network DNN classification model was trained using 35 unique input variables The model was trained and tested
using cross-validation, in which the data were randomly partitioned into training n=3034 and testing n=1012
datasets. Stepwise multivariate regression was further used to identify actual model weights based on predictions
from our trained model.
Results: The overall rate of infection was 1.5 %. The mean area under the curve (AUC), representing the accuracy
of the model, across all 300 iterations was 0.775 (95 % CI [0.767,0.782]) with a median AUC of 0.787. The
positive predictive value (PPV), representing how well the model predicted SSI when a patient had SSI, over all
predictions was 92.56 % with a negative predictive value (NPV), representing how well the model predicted
absence of SSI when a patient did not have SSI, of 98.45 %. In analyzing relative model weights, the five highest
weighted variables were Congestive Heart Failure, Chronic Pulmonary Failure, Hemiplegia/Paraplegia,
Multilevel Fusion and Cerebrovascular Disease respectively. Notable factors that were protective against infection
were ICU Admission, Increasing Charlson Comorbidity Score, Race (White), and being male. Minimally
invasive surgery (MIS) was also determined to be mildly protective.
Conclusion: Machine learning and artificial intelligence are relevant and impressive tools that should be employed
in the clinical decision making for patients. The variables with the largest model weights were primarily
comorbidity related with the exception of multilevel fusion. Further study is needed, however, in order to draw
any definitive conclusions. Keywords: Artificial intelligence | Spine surgery | Surgical site infection |
مقاله انگلیسی |
8 |
Classification of glomerular pathological findings using deep learning and nephrologist–AI collective intelligence approach
طبقه بندی یافته های پاتولوژیک گلومرولی با استفاده از یادگیری عمیق و رویکرد هوش جمعی نفرولوژیست-هوش مصنوعی-2020 Background: Automated classification of glomerular pathological findings is potentially beneficial in establishing
an efficient and objective diagnosis in renal pathology. While previous studies have verified the artificial intelligence
(AI) models for the classification of global sclerosis and glomerular cell proliferation, there are several
other glomerular pathological findings required for diagnosis, and the comprehensive models for the classification
of these major findings have not yet been reported. Whether the cooperation between these AI models and
clinicians improves diagnostic performance also remains unknown. Here, we developed AI models to classify
glomerular images for major findings required for pathological diagnosis and investigated whether those models
could improve the diagnostic performance of nephrologists.
Methods: We used a dataset of 283 kidney biopsy cases comprising 15,888 glomerular images that were annotated
by a total of 25 nephrologists. AI models to classify seven pathological findings: global sclerosis, segmental
sclerosis, endocapillary proliferation, mesangial matrix accumulation, mesangial cell proliferation,
crescent, and basement membrane structural changes, were constructed using deep learning by fine-tuning of
InceptionV3 convolutional neural network. Subsequently, we compared the agreement to truth labels between
majority decision among nephrologists with or without the AI model as a voter.
Results: Our model for global sclerosis showed high performance (area under the curve: periodic acid-Schiff,
0.986; periodic acid methenamine silver, 0.983); the models for the other findings also showed performance
close to those of nephrologists. By adding the AI model output to majority decision among nephrologists, out of
the 14 constructed models, the results of the majority decision showed improvement in sensitivity for 10 models
(four of them were statistically significant) and specificity for eight models (five significant).
Conclusion: Our study showed a proof-of-concept for the classification of multiple glomerular findings in a
comprehensive method of deep learning and suggested its potential effectiveness in improving diagnostic accuracy
of clinicians. Keywords: Renal pathology | Artificial intelligence | Deep learning | Collective intelligence |
مقاله انگلیسی |
9 |
Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods
نقشه برداری بالقوه چشمه آب های زیرزمینی با استفاده از الگوریتم های تکاملی مبتنی بر جمعیت و روش های داده کاوی-2019 Water scarcity inmany regions of theworld has become an unpleasant reality. Groundwater appears to be one of
the main natural resources capable to reverse this situation. Uncovering the spatial patterns of groundwater occurrence
is a crucial factor that could assist in carrying out successful water resources management projects. The
main objective of the current study was to provide a novel methodology approach which utilized Genetic Algorithm(
GA) in order to performa feature selection procedure and data mining methods for generating a groundwater
spring potential map. Three data mining methods, Naïve Bayes (NB), Support Vector Machine (SVM) and
RandomForest (RF) were utilized to construct a groundwater spring potential map that had over 0.81 probability
of occurrence for the Wuqi County, Shaanxi Province, China. Groundwater spring locations and sixteen related
variables were analyzed, namely: lithology, soil cover, land use cover, normalized difference vegetation index
(NDVI), elevation, slope angle, aspect, planform curvature, profile curvature, curvature, stream power index
(SPI), stream transport index (STI), topographic wetness index (TWI), mean annual rainfall, distance from
river network and distance from road network. The Frequency ratio method was used to weight the variables,
whereas a multi-collinearity analysis was performed to identify the relation between the parameters and to decide
about their usage. The optimal set of parameters, which was determined by the GA, reduced the number of
parameters into twelve removing planformcurvature, profile curvature, curvature and STI. The Receiver Operating
Characteristic curve and the area under the curve (AUROC) were estimated so as to evaluate the predictive
power of eachmodel. The results indicated that the optimizedmodels were superior in accuracy than the original
models. The optimized RF model produced the best results (0.9572), followed by the optimized SVM (0.9529)
and the optimized NB (0.8235). Overall, the current study highlights the necessity of applying feature selection
techniques in groundwater spring assessments and also that data miningmethods may be a highly powerful investigation
approach for groundwater spring potential mapping. Keywords: Groundwater spring potential mapping | Genetic algorithm | Naïve Bayes | Support Vector Machine | Random Forest | China |
مقاله انگلیسی |
10 |
Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning
پیش بینی نتایج درمان دارویی ضد صرع بیماران مبتلا به صرع تازه کشف شده با یادگیری ماشین-2019 Objective: The objective of this study was to build a supervised machine learning-based classifier, which can accurately
predict the outcomes of antiepileptic drug (AED) treatment of patients with newly diagnosed epilepsy.
Methods: We collected information from 287 patients with newly diagnosed epilepsy between 2009 and
2017 at the Second Affiliated Hospital of Zhejiang University. Patients were prospectively followed up for at
least 3 years. A number of features, including demographic features,medical history, and auxiliary examinations
(electroencephalogram [EEG] and magnetic resonance imaging [MRI]) are selected to distinguish patients with
different remission outcomes. Seizure outcomes classified as remission and never remission. In addition, remission
is further divided into early remission and late remission. Five classical machine learning algorithms,
i.e., Decision Tree, Random Forest, Support Vector Machine, XGBoost, and Logistic Regression, are selected and
trained by our dataset to get classification models.
Results: Our study shows that 1) comparedwith the other four algorithms, the XGBoost algorithmbased machine
learning model achieves the best prediction performance of the AEDtreatment outcomes between remission and
never remission patientswith an F1 score of 0.947 and an area under the curve (AUC) value of 0.979; 2) The best
discriminative factor for remission and never remission patients is higher number of seizures before treatment
(N3); 3) XGBoost-based machine learning model also offers the best prediction between early remission and
later remission patients, with an F1 score of 0.836 and an AUC value of 0.918; 4) multiple seizure type has the
highest dependence to the categories of early and late remission patients.
Significances: Our XGBoost-based machine learning classifier accurately predicts the most probable AED treatment
outcome of a patient after he/she finishes all the standard examinations for the epilepsy disease. The
classifiers prediction result could help disease guide counseling and eventually improve treatment strategies Keywords: Machine learning | Epilepsy | Epilepsy remission | Antiepileptic drug | Outcome prediction |
مقاله انگلیسی |