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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 |
2 |
Lessons Learned About Autonomous AI: Finding a Safe, Efficacious, and Ethical Path Through the Development Process
درسهایی که درباره هوش مصنوعی مستقل آموخته اند: یافتن راهی ایمن ، کارآمد و اخلاقی از طریق فرایند توسعه-2020 Artificial intelligence (AI) describes systems capable of
making decisions of high cognitive complexity; autonomous
AI systems in healthcare are AI systems that
make clinical decisions without human oversight. Such
rigorously validated medical diagnostic AI systems hold
great promise for improving access to care, increasing accuracy,
and lowering cost, while enabling specialist physicians
to provide the greatest value by managing and
treating patients whose outcomes can be improved.
Ensuring that autonomous AI provides these benefits requires
evaluation of the autonomous AI’s effect on patient
outcome, design, validation, data usage, and
accountability, from a bioethics and accountability
perspective. We performed a literature review of bioethical
principles for AI, and derived evaluation rules for
autonomous AI, grounded in bioethical principles. The
rules include patient outcome, validation, reference standard,
design, data usage, and accountability for medical liability.
Application of the rules explains successful US
Food and Drug Administration (FDA) de novo authorization
of an example, the first autonomous point-of-care
diabetic retinopathy examination de novo authorized by
the FDA, after a preregistered clinical trial. Physicians
need to become competent in understanding the potential
risks and benefits of autonomous AI, and understand its
design, safety, efficacy and equity, validation, and liability,
as well as how its data were obtained. The autonomous
AI evaluation rules introduced here can help
physicians understand limitations and risks as well as
the potential benefits of autonomous AI for their
patients. (Am J Ophthalmol 2020;214:134–142. |
مقاله انگلیسی |
3 |
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 |
4 |
Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review
سیستم های پشتیبانی از تصمیم گیری بالینی برای آزمایش در بخش اورژانس با استفاده از سیستم های هوشمند: یک مرور-2020 Motivation: Emergency Departments’ (ED) modern triage systems implemented worldwide are solely based upon
medical knowledge and experience. This is a limitation of these systems, since there might be hidden patterns
that can be explored in big volumes of clinical historical data. Intelligent techniques can be applied to these data
to develop clinical decision support systems (CDSS) thereby providing the health professionals with objective
criteria. Therefore, it is of foremost importance to identify what has been hampering the application of such
systems for ED triage.
Objectives: The objective of this paper is to assess how intelligent CDSS for triage have been contributing to the
improvement of quality of care in the ED as well as to identify the challenges they have been facing regarding
implementation.
Methods: We applied a standard scoping review method with the manual search of 6 digital libraries, namely:
ScienceDirect, IEEE Xplore, Google Scholar, Springer, MedlinePlus and Web of Knowledge. Search queries were
created and customized for each digital library in order to acquire the information. The core search consisted of
searching in the papers’ title, abstract and key words for the topics “triage”, “emergency department”/“emergency
room” and concepts within the field of intelligent systems.
Results: From the review search, we found that logistic regression was the most frequently used technique for
model design and the area under the receiver operating curve (AUC) the most frequently used performance
measure. Beside triage priority, the most frequently used variables for modelling were patients’ age, gender, vital
signs and chief complaints. The main contributions of the selected papers consisted in the improvement of a
patients prioritization, prediction of need for critical care, hospital or Intensive Care Unit (ICU) admission, ED
Length of Stay (LOS) and mortality from information available at the triage.
Conclusions: In the papers where CDSS were validated in the ED, the authors found that there was an improvement
in the health professionals’ decision-making thereby leading to better clinical management and patients’
outcomes. However, we found that more than half of the studies lacked this implementation phase. We
concluded that for these studies, it is necessary to validate the CDSS and to define key performance measures in
order to demonstrate the extent to which incorporation of CDSS at triage can actually improve care. Keywords: Triage | CDSS | EHR | Machine learning | Critical care |
مقاله انگلیسی |
5 |
Artificial Intelligence in Aortic Surgery: The Rise of the Machine
هوش مصنوعی در جراحی آئورت: ظهور ماشین-2020 The first concept of Artificial Intelligence (AI) came into attention during 1920s
and currently it is rapidly being integrated in our daily clinical practice. The use
of AI has evolved from basic image-based analysis into complex decisions
related to different surgical procedure. AI has been very widely used in the cardiology
field, however the use of such machine-led decisions has been limited
and explored at slower pace in surgical practice. The use of AI in cardiac surgery
is still at its infancy but growing dramatically to reflect the changes in the
clinical decision making process for better patient outcomes. The machine-led
but human controlled algorithms will soon be taking over most of the decision
making processes in cardiac surgery. This review article focuses on the practice
of AI in aortic surgery and the future of such technology-led decision making
pathways on patient outcomes, surgeon’s learning skills and adaptability. Keywords: Big data | Machine learning | Artificial intelligence | Aortic surgery |
مقاله انگلیسی |
6 |
AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app
AI4COVID-19: هوش مصنوعی تشخیص اولیه COVID-19 را از نمونه های سرفه از طریق یک برنامه فعال می کند-2020 Background: The inability to test at scale has become humanity’s Achille’s heel in the ongoing war against the
COVID-19 pandemic. A scalable screening tool would be a game changer. Building on the prior work on coughbased
diagnosis of respiratory diseases, we propose, develop and test an Artificial Intelligence (AI)-powered
screening solution for COVID-19 infection that is deployable via a smartphone app. The app, named AI4COVID-
19 records and sends three 3-s cough sounds to an AI engine running in the cloud, and returns a result within 2
min.
Methods: Cough is a symptom of over thirty non-COVID-19 related medical conditions. This makes the diagnosis
of a COVID-19 infection by cough alone an extremely challenging multidisciplinary problem. We address this
problem by investigating the distinctness of pathomorphological alterations in the respiratory system induced by
COVID-19 infection when compared to other respiratory infections. To overcome the COVID-19 cough training
data shortage we exploit transfer learning. To reduce the misdiagnosis risk stemming from the complex
dimensionality of the problem, we leverage a multi-pronged mediator centered risk-averse AI architecture.
Results: Results show AI4COVID-19 can distinguish among COVID-19 coughs and several types of non-COVID-19
coughs. The accuracy is promising enough to encourage a large-scale collection of labeled cough data to gauge
the generalization capability of AI4COVID-19. AI4COVID-19 is not a clinical grade testing tool. Instead, it offers a
screening tool deployable anytime, anywhere, by anyone. It can also be a clinical decision assistance tool used to
channel clinical-testing and treatment to those who need it the most, thereby saving more lives. Keywords: Artificial intelligence | COVID-19 | Preliminary medical diagnosis | Pre-screening | Public healthcare |
مقاله انگلیسی |
7 |
Clinical Decision Support: The Law, the Future, and the Role for Radiologists
پشتیبانی از تصمیم گیری بالینی: قانون ، آینده و نقش رادیولوژیست-2020 Clinical Decision Support (CDS) was designed as an interactive, electronic tool for use by clinicians that communicates Appropriate Use Criteria (AUC) information to the user and assists them in making the most appropriate treatment decision for a patient’s specific clinical condition. Policymakers recognized AUC as a potential solution to control inappropriate utilization of imaging and made CDS mandatory in the Protecting Access to Medicare Act of 2014. In the years since Protecting Access to Medicare Act, data on the potential impact of CDS has been mixed and much of the physician community has expressed concern about the logistics of the program. This article aims to review the legislation behind the AUC program, the events that have transpired since, and some of the challenges and opportunities facing radiologists in the current environment.br> |
مقاله انگلیسی |
8 |
Determining the probability of juvenile delinquency by using support vector machines and designing a clinical decision support system
تعیین احتمال بزهکاری نوجوانان با استفاده از دستگاه های بردار پشتیبان و طراحی سیستم پشتیبانی از تصمیم گیری بالینی-2020 It is a known fact that individuals who engaged in delinquent behavior in childhood are more probable to carry on similar behavior in adulthood. If the factors that lead children to involve in delinquency are defined, the risk of dragging children into crime can be detected before they are involved in crime and delinquency can be prevented with appropriate preventive rehabilitation programs, in the early period. However, given that delinquent behavior occurs under the influence of multiple conditions and factors rather than a single risk factor; the need for diagnostic tools to evaluate multiple factors together is obvious. Artificial intelligence-based clinical decision support systems have already been used in the field of psychiatry as well as many other fields of medicine. In this study, we assume that thanks to artificial intelligence-based clinical decision support systems, children and adolescents at risk can be detected before the criminal behavior occurs by addressing certain factors. In this way, we anticipate that it can provide psychiatrists and other experts in the field. |
مقاله انگلیسی |
9 |
Why Physiology Is Critical to the Practice of Medicine A 40-year Personal Perspective
چرا فیزیولوژی در عملکرد پزشکی یک پرسپکتیو شخصی 40 ساله بسیار مهم است-2019 Accuracy in diagnosis trumps all other elements in clinical decision making. If diagnosis is inaccurate,
management is likely to prove futile if not dangerous.
The ability to apprehend clues that other clinicians miss depends on mental set (the prepared mind).
Knowledge of physiology provides a periscope for identifying abnormalities beneath the skin
responsible for clinical manifestations on the surface.
Expert diagnosticians suspect disorders based on pattern recognition and automatic retrieval of
knowledge stored in memory. Experts make decisions based on intuition rather than conscious
analytical reasoning. Intuition is the fruit of years of book learning, analytical reasoning, and clinical
practice.
When making routine decisions, physicians typically do not cite mechanistic understanding, but
they call on physiologic principles when confronted with challenging cases.
A superior diagnostician looks at the same findings other clinicians see but thinks of causes that
others have not imagined. Solving clinical mysteries depends on a clinician’s power of imagination,
not the capacity to recite an algorithm or apply a protocol. KEYWORDS : Diagnosis | Clinical reasoning | Intuition | Physical examination | Hyperventilation syndrome |
مقاله انگلیسی |
10 |
Setting up standards: A methodological proposal for pediatric Triage machine learning model construction based on clinical outcomes
تنظیم استانداردها: یک پیشنهاد روش شناختی برای ساخت مدل یادگیری ماشین تراشی کودکان براساس نتایج بالینی-2019 Triage is a critical process in hospital emergency departments (ED). Specifically, we consider how to
achieve fast and accurate patient Triage in the ED of a pediatric hospital. The goal of this paper is to
establish methodological best practices for the application of machine learning (ML) to Triage in pediatric
ED, providing a comprehensive comparison of the performance of ML techniques over a large dataset. Our
work is among the first attempts in this direction. Following very recent works in the literature, we use
the clinical outcome of a case as its label for supervised ML model training, instead of the more uncertain
labels provided by experts.
The experimental dataset contains the records along 3 years of operation of the hospital ED. It consists
of 189,718 patients visits to the hospital. The clinical outcome of 9271 cases (4.98%) wa hospital admission,
therefore our dataset is highly class imbalanced. Our reported performance comparison results
focus on four ML models: Deep Learning (DL), Random Forest (RF), Naive Bayes (NB) and Support Vector
Machines (SVM). Data preprocessing includes class imbalance correction, and case re-labeling. We use
different well known metrics to evaluate performance of ML models in three different experimental settings:
(a) classification of each case into the standard five Triage urgency levels, (b) discrimination of high
versus low case severity according to its clinical outcome, and (c) comparison of the number of patients
assigned to each standard Triage urgency level against the Triage rule based expert system currently in
use at the hospital. RF achieved greater AUC, accuracy, PPV and specificity than the other models in the
dychotomic classification experiments. On the implementation side, our study shows that ML predictive
models trained according to clinical outcomes, provide better Triage performance than the current rule
based expert system in operation at the hospital. Keywords: Machine learning | Emergency department | Triage | Data science | Clinical decision support systems |
مقاله انگلیسی |