A systematic survey of computer-aided diagnosis in medicine: Past and present developments

Yanase, Juri; Triantaphyllou, Evangelos

doi:10.1016/j.eswa.2019.112821

Cited by 218 publications

(113 citation statements)

References 169 publications

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“…Artificial neural network (ANN): Computer-aided diagnosis has advanced due to the application of machine-learning techniques [27]. In particular, our own previous work [28][29][30], as well as numerous works in related literature [31] have demonstrated the effectiveness and modeling flexibility of ANNs for computer-aided diagnosis in medicine.…”

Section: Classification Methodsmentioning

confidence: 99%

Modern machine-learning can support diagnostic differentiation of central and peripheral acute vestibular disorders

et al. 2020

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Background Diagnostic classification of central vs. peripheral etiologies in acute vestibular disorders remains a challenge in the emergency setting. Novel machine-learning methods may help to support diagnostic decisions. In the current study, we tested the performance of standard and machine-learning approaches in the classification of consecutive patients with acute central or peripheral vestibular disorders. Methods 40 Patients with vestibular stroke (19 with and 21 without acute vestibular syndrome (AVS), defined by the presence of spontaneous nystagmus) and 68 patients with peripheral AVS due to vestibular neuritis were recruited in the emergency department, in the context of the prospective EMVERT trial (EMergency VERTigo). All patients received a standardized neuro-otological examination including videooculography and posturography in the acute symptomatic stage and an MRI within 7 days after symptom onset. Diagnostic performance of state-of-the-art scores, such as HINTS (Head Impulse, gazeevoked Nystagmus, Test of Skew) and ABCD 2 (Age, Blood, Clinical features, Duration, Diabetes), for the differentiation of vestibular stroke vs. peripheral AVS was compared to various machine-learning approaches: (i) linear logistic regression (LR), (ii) non-linear random forest (RF), (iii) artificial neural network, and (iv) geometric deep learning (Single/MultiGMC). A prospective classification was simulated by tenfold cross-validation. We analyzed whether machine-estimated feature importances correlate with clinical experience. Results Machine-learning methods (e.g., MultiGMC) outperform univariate scores, such as HINTS or ABCD 2 , for differentiation of all vestibular strokes vs. peripheral AVS (MultiGMC area-under-the-curve (AUC): 0.96 vs. HINTS/ABCD 2 AUC: 0.71/0.58). HINTS performed similarly to MultiGMC for vestibular stroke with AVS (AUC: 0.86), but more poorly for vestibular stroke without AVS (AUC: 0.54). Machine-learning models learn to put different weights on particular features, each of which is relevant from a clinical viewpoint. Established non-linear machine-learning methods like RF and linear methods like LR are less powerful classification models (AUC: 0.89 vs. 0.62). Conclusions Established clinical scores (such as HINTS) provide a valuable baseline assessment for stroke detection in acute vestibular syndromes. In addition, machine-learning methods may have the potential to increase sensitivity and selectivity in the establishment of a correct diagnosis. Keywords Acute vestibular syndrome • HINTS • Machine-learning • MRI • Vestibular neuritis • Vestibular stroke Abbreviations ABCD 2 Age, blood pressure, clinical features, duration, diabetes ANN Artificial neural network AUC Area-under-the-curve AVS Acute vestibular syndrome CVRF Cardiovascular risk factors DT Decision tree DWI Diffusion weighted images ED Emergency department EMVERT EMergency VERTigo FLAIR Fluid attenuated inversion recovery GMC Geometric matrix completion HINTS Head impulse, gaze-evoked nystagmus, test of skew Seyed-Ahmad Ahmadi an...

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Section: Classification Methodsmentioning

confidence: 99%

Modern machine-learning can support diagnostic differentiation of central and peripheral acute vestibular disorders

et al. 2020

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“…A recent development in this history relates to the fact that, due to the generalized expansion of HISs, increasing volumes of data about patients are being recorded, flooding physicians under data (Pivovarov and Elhadad, 2015). This large amount of data quickly proved to be too difficult to analyze by human brains (Yanase and Triantaphyllou, 2019). Data mining and machine learning algorithms are better suited to this task, thanks to their distinctive efficiency when it comes to treating large amounts of data, unveiling correlations, approximating risk functions, or solving classification problems.…”

Section: A Loss Of Confidence In Physicians' Diagnostic Skills and Knmentioning

confidence: 99%

“…These systems will be our main topic in the present article. According to a recent systematic survey of DDSSs (Yanase and Triantaphyllou, 2019), there are currently two main types of DDSSs:…”

Section: Introductionmentioning

confidence: 99%

“…Other examples of clinical guidelines can be found on the NICE website 3 , on the website of the French "Haute Autorité de Santé" (HAS) 4 or in reports of International Classification of Diseases (World Health Organization et al, 1992). Guideline-based DDSSs encompass "expert systems", which integrate "gold-standard" flowcharts/rules into their process to produce full-fledged diagnosis recommendations to physicians (Yanase and Triantaphyllou, 2019), but also systems that prescribe to physicians the steps they should follow to abide by the "gold-standard" (this is the case, for example, of the systems found on the NICE website or the Quick Medical Reference (QMR) linked to the INTERNIST expert system (Miller et al, 1986;Miller, 2010)). -DDSSs based on Machine Learning (ML) algorithms, or ML-based DDSSs, are used to support diagnoses of specific diseases, with the aim to minimize error rates by treating large amounts of data on patients (Dua et al, 2014;Yanase and Triantaphyllou, 2019).…”

Section: Introductionmentioning

confidence: 99%

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What does it mean to provide decision support to a responsible and competent expert?

Richard

Mayag

Talbot

et al. 2020

EURO Journal on Decision Processes

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Decision support consists in helping a decision-maker to improve his/her decisions. However, clients requesting decision support are often themselves experts and are often taken by third parties and/or the general public to be responsible for the decisions they make. This predicament raises complex challenges for decision analysts, who have to avoid infringing upon the expertise and responsibility of the decision-maker. The case of diagnosis decision support in healthcare contexts is particularly illustrative. To support clinicians in their work and minimize the risk of medical error, various decision support systems have been developed, as part of information systems that are now ubiquitous in healthcare contexts. To develop, in collaboration with the hospitals of Lyon, a diagnostic decision support system for day-today customary consultations, we propose in this paper a critical analysis of current approaches to diagnostic decision support, which mainly consist in providing them with guidelines or even full-fledged diagnosis recommendations. We highlight that the use of such decision support systems by physicians raises responsibility issues, but also that it is at odds with the needs and constraints of customary consultations. We argue that the historical choice to favor guidelines or recommendations to physicians implies a very specific vision of what it means to support physicians, and we argue that the flaws of this vision partially ex-A.

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“…They also process and apply filters to these signals to remove unwanted noise. Ye Yuan, ext al, 2017 [24]: proposed a multi-context learning approach for EEG epileptic seizure detection .They generated EEG sequences from EEG records by using wave transduction to describe the frequency content over time. The experimental results indicate that the representative context features can be obtained from the suggested model from a number of viewpoints and thus enhance the efficiency of EEG seizure detection.…”

Section: Related Workmentioning

confidence: 99%

Epileptic Seizure Diagnosis Using EEG Signals

Alwain¹,

Sahib²

2020

IJCSMC

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To measure and track human performance and physiological state, a great number of biomedical data such as the electroencephalographic (EEG) signal is collected from the human body every day. For research and for medical diagnosis and treatment, understanding these signals is critical. There are many research and development projects Within this field, but there are weaknesses point, including their reliance on specific registration technologies, low accuracy and timing of implementations. In This paper a computeraided system are used for diagnosing epileptic seizures more accurately using electronic imaging data. The framework relies on identifying (EEG) signals for use in linear and non-linear applications. This study was conducted using six algorithms of machine learning algorithms which are logistic regression (LR), support vector machine (SVM), k-Nearest Neighbors (KNN), Gaussian Naive Bayes, Artificial Neural Networks (ANN), Principal Component Analysis(PCA). Also, improve surrogate data technique are used for feature extractions that depend on Fourier transforms (FT). It was concluded that the classifier (SVM) works better and showed high accuracy in the classification of data taken for all epilepsy cases.

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A systematic survey of computer-aided diagnosis in medicine: Past and present developments

Cited by 218 publications

References 169 publications

Modern machine-learning can support diagnostic differentiation of central and peripheral acute vestibular disorders

Modern machine-learning can support diagnostic differentiation of central and peripheral acute vestibular disorders

What does it mean to provide decision support to a responsible and competent expert?

Epileptic Seizure Diagnosis Using EEG Signals

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