scholarly journals Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1606
Author(s):  
Jose M. Castillo T. ◽  
Muhammad Arif ◽  
Wiro J. Niessen ◽  
Ivo G. Schoots ◽  
Jifke F. Veenland
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Clinical Decision Making ◽  
External Validation ◽  
Clinical Decision ◽  
Interquartile Range ◽  
Added Value ◽  
Receiver Operating Curve ◽  
Learning Approaches

Significant prostate carcinoma (sPCa) classification based on MRI using radiomics or deep learning approaches has gained much interest, due to the potential application in assisting in clinical decision-making. Objective: To systematically review the literature (i) to determine which algorithms are most frequently used for sPCa classification, (ii) to investigate whether there exists a relation between the performance and the method or the MRI sequences used, (iii) to assess what study design factors affect the performance on sPCa classification, and (iv) to research whether performance had been evaluated in a clinical setting Methods: The databases Embase and Ovid MEDLINE were searched for studies describing machine learning or deep learning classification methods discriminating between significant and nonsignificant PCa on multiparametric MRI that performed a valid validation procedure. Quality was assessed by the modified radiomics quality score. We computed the median area under the receiver operating curve (AUC) from overall methods and the interquartile range. Results: From 2846 potentially relevant publications, 27 were included. The most frequent algorithms used in the literature for PCa classification are logistic regression (22%) and convolutional neural networks (CNNs) (22%). The median AUC was 0.79 (interquartile range: 0.77–0.87). No significant effect of number of included patients, image sequences, or reference standard on the reported performance was found. Three studies described an external validation and none of the papers described a validation in a prospective clinical trial. Conclusions: To unlock the promising potential of machine and deep learning approaches, validation studies and clinical prospective studies should be performed with an established protocol to assess the added value in decision-making.

scholarly journals Deep learning in cancer pathology: a new generation of clinical biomarkers

2020 ◽  
Author(s):  
Amelie Echle ◽  
Niklas Timon Rindtorff ◽  
Titus Josef Brinker ◽  
Tom Luedde ◽  
Alexander Thomas Pearson ◽  
...  
Keyword(s):  
Decision Making ◽  
Image Analysis ◽  
Deep Learning ◽  
Clinical Decision Making ◽  
External Validation ◽  
Therapy Response ◽  
Clinical Decision ◽  
Tumour Tissue ◽  
Molecular Features ◽  
Clinical Biomarkers

AbstractClinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, biomarkers often require tumour tissue on top of routine diagnostic material. Nevertheless, routinely available tumour tissue contains an abundance of clinically relevant information that is currently not fully exploited. Advances in deep learning (DL), an artificial intelligence (AI) technology, have enabled the extraction of previously hidden information directly from routine histology images of cancer, providing potentially clinically useful information. Here, we outline emerging concepts of how DL can extract biomarkers directly from histology images and summarise studies of basic and advanced image analysis for cancer histology. Basic image analysis tasks include detection, grading and subtyping of tumour tissue in histology images; they are aimed at automating pathology workflows and consequently do not immediately translate into clinical decisions. Exceeding such basic approaches, DL has also been used for advanced image analysis tasks, which have the potential of directly affecting clinical decision-making processes. These advanced approaches include inference of molecular features, prediction of survival and end-to-end prediction of therapy response. Predictions made by such DL systems could simplify and enrich clinical decision-making, but require rigorous external validation in clinical settings.

scholarly journals Explaining deep neural networks for knowledge discovery in electrocardiogram analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Steven A. Hicks ◽  
Jonas L. Isaksen ◽  
Vajira Thambawita ◽  
Jonas Ghouse ◽  
Gustav Ahlberg ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Clinical Decision Making ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Medical Data ◽  
Medical Doctors ◽  
Medical Tests ◽  
Deep Learning Model

AbstractDeep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.

Machine learning in clinical decision making

Med ◽  
2021 ◽  
Author(s):  
Lorenz Adlung ◽  
Yotam Cohen ◽  
Uria Mor ◽  
Eran Elinav
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision

scholarly journals Predicting mortality in adults with suspected infection in a Rwandan hospital: an evaluation of the adapted MEWS, qSOFA and UVA scores

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e040361
Author(s):  
Amanda Klinger ◽  
Ariel Mueller ◽  
Tori Sutherland ◽  
Christophe Mpirimbanyi ◽  
Elie Nziyomaze ◽  
...  
Keyword(s):  
Decision Making ◽  
Quality Improvement ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Tertiary Hospital ◽  
Early Warning Score ◽  
Receiver Operating Curve ◽  
Predictive Capacity ◽  
Suspected Infection ◽  
Significant Difference

RationaleMortality prediction scores are increasingly being evaluated in low and middle income countries (LMICs) for research comparisons, quality improvement and clinical decision-making. The modified early warning score (MEWS), quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA), and Universal Vital Assessment (UVA) score use variables that are feasible to obtain, and have demonstrated potential to predict mortality in LMIC cohorts.ObjectiveTo determine the predictive capacity of adapted MEWS, qSOFA and UVA in a Rwandan hospital.Design, setting, participants and outcome measuresWe prospectively collected data on all adult patients admitted to a tertiary hospital in Rwanda with suspected infection over 7 months. We calculated an adapted MEWS, qSOFA and UVA score for each participant. The predictive capacity of each score was assessed including sensitivity, specificity, positive and negative predictive value, OR, area under the receiver operating curve (AUROC) and performance by underlying risk quartile.ResultsWe screened 19 178 patient days, and enrolled 647 unique patients. Median age was 35 years, and in-hospital mortality was 18.1%. The proportion of data missing for each variable ranged from 0% to 11.7%. The sensitivities and specificities of the scores were: adapted MEWS >4, 50.4% and 74.9%, respectively; qSOFA >2, 24.8% and 90.4%, respectively; and UVA >4, 28.2% and 91.1%, respectively. The scores as continuous variables demonstrated the following AUROCs: adapted MEWS 0.69 (95% CI 0.64 to 0.74), qSOFA 0.65 (95% CI 0.60 to 0.70), and UVA 0.71 (95% CI 0.66 to 0.76); there was no statistically significant difference between the discriminative capacities of the scores.ConclusionThree scores demonstrated a modest ability to predict mortality in a prospective study of inpatients with suspected infection at a Rwandan tertiary hospital. Careful consideration must be given to their adequacy before using them in research comparisons, quality improvement or clinical decision-making.

Quality specifications: self pleasure for clinical laboratories or added value for patient management?

2007 ◽  
Vol 45 (4) ◽  
Author(s):  
Mario Plebani
Keyword(s):  
Decision Making ◽  
Patient Management ◽  
Clinical Decision Making ◽  
Laboratory Data ◽  
Clinical Decision ◽  
Added Value ◽  
Internal Quality ◽  
Laboratory Services ◽  
Management Procedures ◽  
Quality Specifications

AbstractAnalytical quality specifications play a key role in assuring and continuously improving high-quality laboratory services. However, I believe, that there are two “missing links” in the effective management of quality specifications in the delivery of laboratory services. The first is the evidence that pre-analytical variation and related problems are not taken into great consideration by laboratory professionals. The second missing link is the communication of quality specifications to clinicians and other possible stakeholders. If quality specifications represent “the level of performance required to facilitate clinical decision-making”, they cannot be used only for internal quality management procedures but must be communicated to facilitate clinical reasoning, decision-making and patient management. A consensus should be achieved in the scientific community on these issues to assure better utilization of laboratory data and, ultimately, improved clinical outcomes.Clin Chem Lab Med 2007;45:462–6.

scholarly journals Machine learning to assist clinical decision-making during the COVID-19 pandemic

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Shubham Debnath ◽  
◽  
Douglas P. Barnaby ◽  
Kevin Coppa ◽  
Alexander Makhnevich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision

scholarly journals Deep Learning of Markov Model Based Machines for Determination of Better Treatment Option Decisions for Infertile Women

2019 ◽  
Author(s):  
Arni S.R. Srinivasa Rao ◽  
Michael P. Diamond
Keyword(s):  
Mathematical Modeling ◽  
Machine Learning ◽  
Deep Learning ◽  
Clinical Decision Making ◽  
Treatment Options ◽  
Clinical Decision ◽  
Infertile Women ◽  
Learning Techniques ◽  
Technical Article

AbstractIn this technical article, we are proposing ideas those we have been developing of how machine learning and deep learning techniques can potentially assist obstetricians / gynecologists in better clinical decision making using infertile women in their treatment options in combination with mathematical modeling in pregnant women as examples.

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