Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023)

Seoni, Silvia; Jahmunah, Vicnesh; Salvi, Massimo; Barua, Prabal Datta; Molinari, Filippo; Acharya, U. Rajendra

doi:10.1016/j.compbiomed.2023.107441

Cited by 57 publications

(13 citation statements)

References 119 publications

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“…Bayesian networks, Dempster‐Shafer theory, and fuzzy logic techniques can be employed to make accurate decisions 53 . Uncertainty quantification (UQ) can be used to assess the uncertainty due to deep learning models or data 54,55 . This UQ can help to minimize the influence of noise or change in the model parameters.…”

Section: Discussionmentioning

confidence: 99%

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Sleep disorder identification using wavelet scattering on ECG signals

Sharma,

Lodhi,

Yadav

et al. 2023

Int J Imaging Syst Tech

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Sleep affects the functioning of all biological processes in the human body. Sleep disorders disrupt the well‐being of an individual and can potentially lead to other health complications. It affects a large part of the population, and their timely and efficient detection is crucial for improving the quality of health. Sleep disorders are generally analyzed using polysomnogram (PSG) signals, which must be captured in sophisticated laboratory settings. However, electrocardiogram (ECG) signals provide information about the electrical activity of the heart, which is strongly linked with disturbances in sleep and can be used as an indicator of disordered sleep. Along with that, the recording of ECG signals is much easier than other signals and can be done remotely without causing any significant disturbance to the subject. This brings practicality to the systems that are already being employed for sleep disorder detection. In this paper, we present a method for identifying types of sleep disorders using ECG signals. The objective of this paper is to investigate the use of ECG signals for the automated identification of insomnia, narcolepsy, periodic leg movement (PLM), rapid eye movement (REM) behavior disorder (RBD), and nocturnal frontal lobe epilepsy (NFLE) against healthy subjects. We aim to develop a machine learning‐based algorithm that automatically classifies ECG signals into these sleep disorder categories. The cyclic alternating pattern (CAP) sleep database was used in this study, which contains PSG recordings from individuals with and without sleep disorders, including insomnia, narcolepsy, PLM, RBD, and NFLE. A wavelet scattering network has been used to extract features from the ECG signals. Various classifiers were tested, and the ensemble bag of trees classifier provided the optimum performance. An overall accuracy of 98% was obtained for the identification of sleep disorders, along with a classification accuracy of 99.37%, 99.45%, 99.23%, 99.4%, and 99.65% for insomnia, narcolepsy, NFLE, PLM, and RBD, respectively, for binary classification against healthy subject data. Our results show that wavelet scattering network and ensemble of bagged tree (EbagT) classification can accurately identify various sleep disorders. They provided evidence that ECG signals can be used to identify and diagnose sleep disorders, which could lead to the development of accurate detection of these sleep disorders. Also, our study demonstrated the effectiveness of the proposed algorithm in identifying multiple sleep disorders, which could lead to more efficient and cost‐effective diagnosis and treatment of sleep disorders.

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Section: Discussionmentioning

confidence: 99%

“…53 Uncertainty quantification (UQ) can be used to assess the uncertainty due to deep learning models or data. 54,55 This UQ can help to minimize the influence of noise or change in the model parameters.…”

Section: Discussionmentioning

confidence: 99%

Sleep disorder identification using wavelet scattering on ECG signals

Sharma,

Lodhi,

Yadav

et al. 2023

Int J Imaging Syst Tech

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“…Chen et al, 2022). Given uncertainty quantification's success in other healthcare fields (Seoni et al, 2023), research should focus on integrating uncertainty more in AF analysis.…”

Section: Promising Approaches For Real-time and Continuous Af Monitoringmentioning

confidence: 99%

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Salvi,

Acharya,

Seoni

et al. 2024

WIREs Data Min & Knowl

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Atrial fibrillation (AF) affects more than 30 million individuals worldwide, making it the most prevalent cardiac arrhythmia on a global scale. This systematic review summarizes recent advancements in applying artificial intelligence (AI) techniques for AF detection, prediction, and guiding treatment selection and risk stratification. In adherence with the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses), a total of 171 pertinent studies conducted between 2013 and 2023 were examined. Studies applying machine learning (ML) and deep learning (DL) to electrocardiogram (ECG), photoplethysmography (PPG), wearable data, and other sources were evaluated. For AF detection, most works employed DL (48 studies) and ML (28 studies) on ECG data. DL methods directly analyzed ECG waveforms and outperformed approaches relying on hand‐crafted features. For prediction and risk stratification, 22 studies used ML while 7 leveraged DL on ECG. An emerging trend showed the growing potential of PPG for AF screening. Overall, AI demonstrated promising capabilities across various AF‐related tasks. However, real‐world implementation faces challenges including a lack of interpretability, the need for multimodal data integration, prospective performance validation, and regulatory compliance. Future research directions involve quantifying model uncertainty, enhancing transparency, and conducting population‐based clinical trials to facilitate translation into practice.This article is categorized under: Application Areas > Health Care Application Areas > Science and Technology Technologies > Artificial Intelligence

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“…Numerous approaches have been proposed in the literature for generating probability and uncertainty maps (Li 2018, McCrindle et al 2021, Seoni et al 2023. Monte Carlo (MC) dropout suggested in 2015 by Gal and Ghahramani (2015) represents one of the most widely adopted techniques due to its straightforward implementation (Bhat et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Clinical assessment of deep learning-based uncertainty maps in lung cancer segmentation

Maruccio,

Eppinga,

Laves

et al. 2024

Phys. Med. Biol.

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Objective. Prior to radiation therapy planning, accurate delineation of gross tumour volume (GTVs) and organs at risk (OARs) is crucial. In the current clinical practice, tumour delineation is performed manually by radiation oncologists, which is time-consuming and prone to large inter-observer variability. With the advent of deep learning (DL) models, automated contouring has become possible, speeding up procedures and assisting clinicians. However, these tools are currently used in the clinic mostly for contouring OARs, since these systems are not reliable yet for contouring GTVs. To improve the reliability of these systems, researchers have started exploring the topic of probabilistic neural networks. However, there is still limited knowledge of the practical implementation of such networks in real clinical settings. Approach. In this work, we developed a 3D probabilistic system that generates DL-based uncertainty maps for lung cancer CT segmentations. We employed the Monte Carlo (MC) dropout technique to generate probabilistic and uncertainty maps, while the model calibration was evaluated by using reliability diagrams. A clinical validation was conducted in collaboration with a radiation oncologist to qualitatively assess the value of the uncertainty estimates. We also proposed two novel metrics, namely mean uncertainty (MU) and relative uncertainty volume (RUV), as potential indicators for clinicians to assess the need for independent visual checks of the DL-based segmentation. Main results. Our study showed that uncertainty mapping effectively identified cases of under or over-contouring. Although the overconfidence of the model, a strong correlation was observed between the clinical opinion and MU metric. Moreover, both MU and RUV revealed high AUC values in discretising between low and high uncertainty cases. Significance. Our study is one of the first attempts to clinically validate uncertainty estimates in DL-based contouring. The two proposed metrics exhibited promising potential as indicators for clinicians to independently assess the quality of tumour delineation.

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Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023)

Cited by 57 publications

References 119 publications

Sleep disorder identification using wavelet scattering on ECG signals

Sleep disorder identification using wavelet scattering on ECG signals

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Clinical assessment of deep learning-based uncertainty maps in lung cancer segmentation

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