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Liang C, Pan S, Wu W, Chen F, Zhang C, Zhou C, Gao Y, Ruan X, Quan S, Zhao Q, Pan J. Glucocorticoid therapy for sepsis in the AI era: a survey on current and future approaches. Comput Struct Biotechnol J 2024; 24:292-305. [PMID: 38681133 PMCID: PMC11047203 DOI: 10.1016/j.csbj.2024.04.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/06/2024] [Accepted: 04/07/2024] [Indexed: 05/01/2024] Open
Abstract
Sepsis, a life-threatening medical condition, manifests as new or worsening organ failures due to a dysregulated host response to infection. Many patients with sepsis have manifested a hyperinflammatory phenotype leading to the identification of inflammatory modulation by corticosteroids as a key treatment modality. However, the optimal use of corticosteroids in sepsis treatment remains a contentious subject, necessitating a deeper understanding of their physiological and pharmacological effects. Our study conducts a comprehensive review of randomized controlled trials (RCTs) focusing on traditional corticosteroid treatment in sepsis, alongside an analysis of evolving clinical guidelines. Additionally, we explore the emerging role of artificial intelligence (AI) in medicine, particularly in diagnosing, prognosticating, and treating sepsis. AI's advanced data processing capabilities reveal new avenues for enhancing corticosteroid therapeutic strategies in sepsis. The integration of AI in sepsis treatment has the potential to address existing gaps in knowledge, especially in the application of corticosteroids. Our findings suggest that combining corticosteroid therapy with AI-driven insights could lead to more personalized and effective sepsis treatments. This approach holds promise for improving clinical outcomes and presents a significant advancement in the management of this complex and often fatal condition.
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Affiliation(s)
- Chenglong Liang
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
- Wenzhou Medical University, Wenzhou 325000, China
- School of Nursing, Wenzhou Medical University, Wenzhou 325000, China
| | - Shuo Pan
- Wenzhou Medical University, Wenzhou 325000, China
| | - Wei Wu
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China
| | - Fanxuan Chen
- Wenzhou Medical University, Wenzhou 325000, China
- School of Biomedical Engineering, School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou 325000, China
| | - Chengxi Zhang
- School of Materials Science and Engineering, Shandong Jianzhu University, Jinan 250101, China
| | - Chen Zhou
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Yifan Gao
- Department of Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Xiangyuan Ruan
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Shichao Quan
- Department of Big Data in Health Science, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China
| | - Jingye Pan
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
- Department of Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
- Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province, Wenzhou 325000, China
- Wenzhou Key Laboratory of Critical Care and Artificial Intelligence, Wenzhou 325000, China
- Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization, Wenzhou 325000, China
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Li C, Hui D, Wu F, Xia Y, Shi F, Yang M, Zhang J, Peng C, Feng J, Li C. Automatic diagnosis of Parkinson's disease using artificial intelligence base on routine T1-weighted MRI. Front Med (Lausanne) 2024; 10:1303501. [PMID: 38249966 PMCID: PMC10797132 DOI: 10.3389/fmed.2023.1303501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 12/08/2023] [Indexed: 01/23/2024] Open
Abstract
Background Parkinson's disease (PD) is the second most common neurodegenerative disease. An objective diagnosis method is urgently needed in clinical practice. In this study, deep learning and radiomics techniques were studied to automatically diagnose PD from healthy controls (HCs). Methods 155 PD patients and 154 HCs were randomly divided into a training set (246 patients) and a testing set (63 patients). The brain subregions identification and segmentation were automatically performed with a VB-net, and radiomics features of billateral thalamus, caudatum, putamen and pallidum were extracted. Five independent machine learning classifiers [Support Vector Machine (SVM), Stochastic gradient descent (SGD), random forest (RF), quadratic discriminant analysis (QDA) and decision tree (DT)] were trained on the training set, and validated on the testing. Delong test was used to compare the performance of different models. Results Our VB-net could automatically identify and segment the brain into 109 regions. 2,264 radiomics features were automatically extracted from the billateral thalamus, caudatum, putamen or pallidum of each patient. After four step of features dimensionality reduction, Delong tests showed that the SVM model based on combined features had the best performance, with AUCs of 0.988 (95% CI: 0.979 ~ 0.998, specificity = 91.1%, sensitivity =100%, accuracy = 89.4% and precision = 88.2%) and 0.976 (95% CI: 0.942 ~ 1.000, specificity = 100%, sensitivity = 87.1%, accuracy = 93.5% and precision = 88.6%) in the training set and testing set, respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high. Conclusion The SVM model based on combined features could be used to diagnose PD with high accuracy. Our fully automatic model could rapidly process the MRI data and distinguish PD and HCs in one minute. It greatly improved the diagnostic efficiency and has a great potential value in clinical practice to help the early diagnosis of PD.
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Affiliation(s)
- Chang Li
- Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Dongming Hui
- Department of Radiology, Chongqing Western Hospital, Chongqing, China
| | - Faqi Wu
- Department of Medical Service, Yanzhuang Central Hospital of Gangcheng District, Chongqing, China
| | - Yuwei Xia
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd. Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd. Shanghai, China
| | - Mingguang Yang
- Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Jinrui Zhang
- Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Chao Peng
- Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Junbang Feng
- Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Chuanming Li
- Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
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Wani NA, Kumar R, Bedi J. DeepXplainer: An interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107879. [PMID: 37897989 DOI: 10.1016/j.cmpb.2023.107879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 10/17/2023] [Accepted: 10/20/2023] [Indexed: 10/30/2023]
Abstract
BACKGROUND AND OBJECTIVE Artificial intelligence (AI) has several uses in the healthcare industry, some of which include healthcare management, medical forecasting, practical making of decisions, and diagnosis. AI technologies have reached human-like performance, but their use is limited since they are still largely viewed as opaque black boxes. This distrust remains the primary factor for their limited real application, particularly in healthcare. As a result, there is a need for interpretable predictors that provide better predictions and also explain their predictions. METHODS This study introduces "DeepXplainer", a new interpretable hybrid deep learning-based technique for detecting lung cancer and providing explanations of the predictions. This technique is based on a convolutional neural network and XGBoost. XGBoost is used for class label prediction after "DeepXplainer" has automatically learned the features of the input using its many convolutional layers. For providing explanations or explainability of the predictions, an explainable artificial intelligence method known as "SHAP" is implemented. RESULTS The open-source "Survey Lung Cancer" dataset was processed using this method. On multiple parameters, including accuracy, sensitivity, F1-score, etc., the proposed method outperformed the existing methods. The proposed method obtained an accuracy of 97.43%, a sensitivity of 98.71%, and an F1-score of 98.08. After the model has made predictions with this high degree of accuracy, each prediction is explained by implementing an explainable artificial intelligence method at both the local and global levels. CONCLUSIONS A deep learning-based classification model for lung cancer is proposed with three primary components: one for feature learning, another for classification, and a third for providing explanations for the predictions made by the proposed hybrid (ConvXGB) model. The proposed "DeepXplainer" has been evaluated using a variety of metrics, and the results demonstrate that it outperforms the current benchmarks. Providing explanations for the predictions, the proposed approach may help doctors in detecting and treating lung cancer patients more effectively.
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Affiliation(s)
- Niyaz Ahmad Wani
- Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala (PIN: 147004), Punjab, India.
| | - Ravinder Kumar
- Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala (PIN: 147004), Punjab, India.
| | - Jatin Bedi
- Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala (PIN: 147004), Punjab, India.
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Katmah R, Shehhi AA, Jelinek HF, Hulleck AA, Khalaf K. A Systematic Review of Gait Analysis in the Context of Multimodal Sensing Fusion and AI. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4189-4202. [PMID: 37847624 DOI: 10.1109/tnsre.2023.3325215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
BACKGROUND Neurological diseases are a leading cause of disability and mortality. Gait, or human walking, is a significant predictor of quality of life, morbidity, and mortality. Gait patterns and other kinematic, kinetic, and balance gait features are accurate and powerful diagnostic and prognostic tools. OBJECTIVE This review article focuses on the applicability of gait analysis using fusion techniques and artificial intelligence (AI) models. The aim is to examine the significance of mixing several types of wearable and non-wearable sensor data and the impact of this combination on the performance of AI models. METHOD In this systematic review, 66 studies using more than two modalities to record and analyze gait were identified. 40 studies incorporated multiple gait analysis modalities without the use of artificial intelligence to extract gait features such as kinematic, kinetic, margin of stability, temporal, and spatial gait parameters, as well as cerebral activity. Similarly, 26 studies analyzed gait data using multimodal fusion sensors and AI algorithms. RESULTS The research summarized here demonstrates that the quality of gait analysis and the effectiveness of AI models can both benefit from the integration of data from many sensors. Meanwhile, the utilization of EMG signals in fusion data is especially advantageous. CONCLUSION The findings of this review suggest that a smart, portable, wearable-based gait and balance assessment system can be developed using multimodal sensing of the most cutting-edge, clinically relevant tools and technology available. The information presented in this article may serve as a vital springboard for such development.
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Wu P, Cao B, Liang Z, Wu M. The advantages of artificial intelligence-based gait assessment in detecting, predicting, and managing Parkinson's disease. Front Aging Neurosci 2023; 15:1191378. [PMID: 37502426 PMCID: PMC10368956 DOI: 10.3389/fnagi.2023.1191378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 06/05/2023] [Indexed: 07/29/2023] Open
Abstract
Background Parkinson's disease is a neurological disorder that can cause gait disturbance, leading to mobility issues and falls. Early diagnosis and prediction of freeze episodes are essential for mitigating symptoms and monitoring the disease. Objective This review aims to evaluate the use of artificial intelligence (AI)-based gait evaluation in diagnosing and managing Parkinson's disease, and to explore the potential benefits of this technology for clinical decision-making and treatment support. Methods A thorough review of published literature was conducted to identify studies, articles, and research related to AI-based gait evaluation in Parkinson's disease. Results AI-based gait evaluation has shown promise in preventing freeze episodes, improving diagnosis, and increasing motor independence in patients with Parkinson's disease. Its advantages include higher diagnostic accuracy, continuous monitoring, and personalized therapeutic interventions. Conclusion AI-based gait evaluation systems hold great promise for managing Parkinson's disease and improving patient outcomes. They offer the potential to transform clinical decision-making and inform personalized therapies, but further research is needed to determine their effectiveness and refine their use.
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Affiliation(s)
- Peng Wu
- College of Acupuncture and Orthopedics, Hubei University of Chinese Medicine, Wuhan, Hubei, China
| | - Biwei Cao
- Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
- Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, Hubei, China
- Hubei Academy of Traditional Chinese Medicine, Wuhan, Hubei, China
| | - Zhendong Liang
- College of Acupuncture and Orthopedics, Hubei University of Chinese Medicine, Wuhan, Hubei, China
| | - Miao Wu
- Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
- Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, Hubei, China
- Hubei Academy of Traditional Chinese Medicine, Wuhan, Hubei, China
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Junaid M, Ali S, Eid F, El-Sappagh S, Abuhmed T. Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson's disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 234:107495. [PMID: 37003039 DOI: 10.1016/j.cmpb.2023.107495] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 02/23/2023] [Accepted: 03/17/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND AND OBJECTIVES Parkinson's Disease (PD) is a devastating chronic neurological condition. Machine learning (ML) techniques have been used in the early prediction of PD progression. Fusion of heterogeneous data modalities proved its capability to improve the performance of ML models. Time series data fusion supports the tracking of the disease over time. In addition, the trustworthiness of the resulting models is improved by adding model explainability features. The literature on PD has not sufficiently explored these three points. METHODS In this work, we proposed an ML pipeline for predicting the progression of PD that is both accurate and explainable. We explore the fusion of different combinations of five time series modalities from the Parkinson's Progression Markers Initiative (PPMI) real-world dataset, including patient characteristics, biosamples, medication history, motor, and non-motor function data. Each patient has six visits. The problem has been formulated in two ways: ❶ a three-class based progression prediction with 953 patients in each time series modality, and ❷ a four-class based progression prediction with 1,060 patients in each time series modality. The statistical features of these six visits were calculated from each modality and diverse feature selection methods were applied to select the most informative feature sets. The extracted features were used to train a set of well-known ML models including Support vector machines (SVM), random forests (RF), extra tree classifier (ETC), light gradient boosting machines (LGBM), and stochastic gradient descent (SGD). We examined a number of data-balancing strategies in the pipeline with different combinations of modalities. ML models have been optimized using the Bayesian optimizer. A comprehensive evaluation of various ML methods has been conducted, and the best models have been extended to provide different explainability features. RESULTS We compare the performance of ML models before and after optimization and using and without using feature selection. In the three-class experiment and with various modality fusions, the LGBM model produced the most accurate results with a 10-fold cross-validation (10-CV) accuracy of 90.73% using non-motor function modality. RF produced the best results in the four-class experiment with various modality fusions with a 10-CV accuracy of 94.57% using non-motor modality. With the fused dataset of non-motor and motor function modalities, the LGBM model outperformed the other ML models in both the 3-class and 4-class experiments (i.e., 10-CV accuracy of 94.89% and 93.73%, respectively). Using the Shapely Additive Explanations (SHAP) framework, we employed global and instance-based explanations to explain the behavior of each ML classifier. Moreover, we extended the explainability by implementing the LIME and SHAPASH local explainers. The consistency of these explainers has been explored. The resultant classifiers were accurate, explainable, and thus medically more relevant and applicable. CONCLUSIONS The select modalities and feature sets were confirmed by the literature and medical experts. The various explainers suggest that the bradykinesia (NP3BRADY) feature was the most dominant and consistent. By providing thorough insights into the influence of multiple modalities on the disease risk, the suggested approach is expected to help improve the clinical knowledge of PD progression processes.
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Affiliation(s)
- Muhammad Junaid
- Information Laboratory (InfoLab), Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, South Korea.
| | - Sajid Ali
- Information Laboratory (InfoLab), Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, South Korea.
| | - Fatma Eid
- Technology Management, Stony Brook University, New York 11794, USA.
| | - Shaker El-Sappagh
- Information Laboratory (InfoLab), College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, South Korea; Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt; Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha, 13518, Egypt.
| | - Tamer Abuhmed
- Information Laboratory (InfoLab), College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, South Korea.
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Nour M, Senturk U, Polat K. Diagnosis and classification of Parkinson's disease using ensemble learning and 1D-PDCovNN. Comput Biol Med 2023; 161:107031. [PMID: 37211002 DOI: 10.1016/j.compbiomed.2023.107031] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/05/2023] [Accepted: 05/10/2023] [Indexed: 05/23/2023]
Abstract
In this paper, we proposed a novel approach to diagnose and classify Parkinson's Disease (PD) using ensemble learning and 1D-PDCovNN, a novel deep learning technique. PD is a neurodegenerative disorder; early detection and correct classification are essential for better disease management. The primary aim of this study is to develop a robust approach to diagnosing and classifying PD using EEG signals. As the dataset, we have used the San Diego Resting State EEG dataset to evaluate our proposed method. The proposed method mainly consists of three stages. In the first stage, the Independent Component Analysis (ICA) method has been used as the pre-processing method to filter out the blink noises from the EEG signals. Also, the effect of the band showing motor cortex activity in the 7-30 Hz frequency band of EEG signals in diagnosing and classifying Parkinson's disease from EEG signals has been investigated. In the second stage, the Common Spatial Pattern (CSP) method has been used as the feature extraction to extract useful information from EEG signals. Finally, an ensemble learning approach, Dynamic Classifier Selection (DCS) in Modified Local Accuracy (MLA), has been employed in the third stage, consisting of seven different classifiers. As the classifier method, DCS in MLA, XGBoost, and 1D-PDCovNN classifier has been used to classify the EEG signals as the PD and healthy control (HC). We first used dynamic classifier selection to diagnose and classify Parkinson's disease (PD) from EEG signals, and promising results have been obtained. The performance of the proposed approach has been evaluated using the classification accuracy, F-1 score, kappa score, Jaccard score, ROC curve, recall, and precision values in the classification of PD with the proposed models. In the classification of PD, the combination of DCS in MLA achieved an accuracy of 99,31%. The results of this study demonstrate that the proposed approach can be used as a reliable tool for early diagnosis and classification of PD.
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Affiliation(s)
- Majid Nour
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Umit Senturk
- Department of Computer Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey.
| | - Kemal Polat
- Department of Electrical and Electronics Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey.
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Head-to-Head Comparison Between Rabbit Sign and EANM/SNMMI Criteria for the 18F-DOPA Visual Assessment of Parkinsonian Syndromes in PET/MRI: A Multiple Expert-Based and Blinded Controlled Study. Clin Nucl Med 2023; 48:112-118. [PMID: 36607361 DOI: 10.1097/rlu.0000000000004481] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
PURPOSE The aim of this study was to compare the diagnostic performance of the rabbit visual pattern versus the one endorsed by the EANM/SNMMI for the diagnosis of parkinsonian syndromes in PET/MRI. PATIENTS AND METHODS The 18F-DOPA PET images of 129 consecutive patients (65 Park+ and 64 controls) with 1 year of clinical follow-up were reviewed independently by 5 experienced readers on the same imaging workstation, blinded to the final clinical diagnosis. Two visual methods were assessed independently, with several days to months of interval: the criteria endorsed by EANM/SNMMI and the "rabbit" shape of the striate assessed on 3D MIP images. The sensitivities, specificities, likelihood ratios, and predictive values of the 2 diagnostic tests were estimated simultaneously by using the "comparison of 2 binary diagnostic tests to a paired design" method. RESULTS The estimated 95% confidence interval (CI) of sensitivities and specificities ranged from 49.4% to 76.5% and from 83.2% to 97.7%, respectively. The 95% CI estimates of positive and negative likelihood ratios ranged from 3.8 to 26.7 and from 0.26 to 0.56, respectively. The 95% CI estimates of the positive and negative predictive values ranged from 78.1% to 96.7% and from 60.3% to 81.4%, respectively. For all the parameters, no statistical difference was observed between the 2 methods (P > 0.05). The rabbit sign reduced the readers' discrepancies by 25%, while maintaining the same performance. CONCLUSIONS The rabbit visual pattern appears at least comparable to the current EANM/SNMMI reference procedure for the assessment of parkinsonian syndromes in daily clinical practice, without the need of any image postprocessing. Further multicenter prospective studies would be of relevance to validate these findings.
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Chatzaki C, Skaramagkas V, Kefalopoulou Z, Tachos N, Kostikis N, Kanellos F, Triantafyllou E, Chroni E, Fotiadis DI, Tsiknakis M. Can Gait Features Help in Differentiating Parkinson's Disease Medication States and Severity Levels? A Machine Learning Approach. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249937. [PMID: 36560313 PMCID: PMC9787905 DOI: 10.3390/s22249937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 05/14/2023]
Abstract
Parkinson's disease (PD) is one of the most prevalent neurological diseases, described by complex clinical phenotypes. The manifestations of PD include both motor and non-motor symptoms. We constituted an experimental protocol for the assessment of PD motor signs of lower extremities. Using a pair of sensor insoles, data were recorded from PD patients, Elderly and Adult groups. Assessment of PD patients has been performed by neurologists specialized in movement disorders using the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS)-Part III: Motor Examination, on both ON and OFF medication states. Using as a reference point the quantified metrics of MDS-UPDRS-Part III, severity levels were explored by classifying normal, mild, moderate, and severe levels of PD. Elaborating the recorded gait data, 18 temporal and spatial characteristics have been extracted. Subsequently, feature selection techniques were applied to reveal the dominant features to be used for four classification tasks. Specifically, for identifying relations between the spatial and temporal gait features on: PD and non-PD groups; PD, Elderly and Adults groups; PD and ON/OFF medication states; MDS-UPDRS: Part III and PD severity levels. AdaBoost, Extra Trees, and Random Forest classifiers, were trained and tested. Results showed a recognition accuracy of 88%, 73% and 81% for, the PD and non-PD groups, PD-related medication states, and PD severity levels relevant to MDS-UPDRS: Part III ratings, respectively.
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Affiliation(s)
- Chariklia Chatzaki
- Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Crete, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology—Hellas, Vassilika Vouton, 71110 Heraklion, Crete, Greece
- Correspondence:
| | - Vasileios Skaramagkas
- Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Crete, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology—Hellas, Vassilika Vouton, 71110 Heraklion, Crete, Greece
| | | | - Nikolaos Tachos
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
- Biomedical Research Institute, Foundation for Research and Technology—Hellas, 45110 Ioannina, Greece
| | | | | | | | - Elisabeth Chroni
- Department of Neurology, Patras University Hospital, 26404 Patra, Greece
| | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
- Biomedical Research Institute, Foundation for Research and Technology—Hellas, 45110 Ioannina, Greece
| | - Manolis Tsiknakis
- Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Crete, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology—Hellas, Vassilika Vouton, 71110 Heraklion, Crete, Greece
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Ngo QC, Motin MA, Pah ND, Drotár P, Kempster P, Kumar D. Computerized analysis of speech and voice for Parkinson's disease: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107133. [PMID: 36183641 DOI: 10.1016/j.cmpb.2022.107133] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 09/13/2022] [Accepted: 09/13/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Speech impairment is an early symptom of Parkinson's disease (PD). This study has summarized the literature related to speech and voice in detecting PD and assessing its severity. METHODS A systematic review of the literature from 2010 to 2021 to investigate analysis methods and signal features. The keywords "Automatic analysis" in conjunction with "PD speech" or "PD voice" were used, and the PubMed and ScienceDirect databases were searched. A total of 838 papers were found on the first run, of which 189 were selected. One hundred and forty-seven were found to be suitable for the review. The different datasets, recording protocols, signal analysis methods and features that were reported are listed. Values of the features that separate PD patients from healthy controls were tabulated. Finally, the barriers that limit the wide use of computerized speech analysis are discussed. RESULTS Speech and voice may be valuable markers for PD. However, large differences between the datasets make it difficult to compare different studies. In addition, speech analytic methods that are not informed by physiological understanding may alienate clinicians. CONCLUSIONS The potential usefulness of speech and voice for the detection and assessment of PD is confirmed by evidence from the classification and correlation results.
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Affiliation(s)
| | - Mohammod Abdul Motin
- Biosignals Lab, RMIT University, Melbourne, Australia; Department of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Nemuel Daniel Pah
- Biosignals Lab, RMIT University, Melbourne, Australia; Universitas Surabaya, Indonesia
| | - Peter Drotár
- Intelligent Information Systems Lab, Technical University of Kosice, Letna 9, 42001, Kosice, Slovakia
| | - Peter Kempster
- Neurosciences Department, Monash Health, Clayton, VIC, Australia; Department of Medicine, School of Clinical Sciences, Monash University, Clayton, VIC, Australia
| | - Dinesh Kumar
- Biosignals Lab, RMIT University, Melbourne, Australia.
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Loh HW, Ooi CP, Seoni S, Barua PD, Molinari F, Acharya UR. Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107161. [PMID: 36228495 DOI: 10.1016/j.cmpb.2022.107161] [Citation(s) in RCA: 88] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/16/2022] [Accepted: 09/25/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVES Artificial intelligence (AI) has branched out to various applications in healthcare, such as health services management, predictive medicine, clinical decision-making, and patient data and diagnostics. Although AI models have achieved human-like performance, their use is still limited because they are seen as a black box. This lack of trust remains the main reason for their low use in practice, especially in healthcare. Hence, explainable artificial intelligence (XAI) has been introduced as a technique that can provide confidence in the model's prediction by explaining how the prediction is derived, thereby encouraging the use of AI systems in healthcare. The primary goal of this review is to provide areas of healthcare that require more attention from the XAI research community. METHODS Multiple journal databases were thoroughly searched using PRISMA guidelines 2020. Studies that do not appear in Q1 journals, which are highly credible, were excluded. RESULTS In this review, we surveyed 99 Q1 articles covering the following XAI techniques: SHAP, LIME, GradCAM, LRP, Fuzzy classifier, EBM, CBR, rule-based systems, and others. CONCLUSION We discovered that detecting abnormalities in 1D biosignals and identifying key text in clinical notes are areas that require more attention from the XAI research community. We hope this is review will encourage the development of a holistic cloud system for a smart city.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Silvia Seoni
- Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino 10129, Italy
| | - Prabal Datta Barua
- Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino 10129, Italy
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia; School of Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan.
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Bernardo LS, Damaševičius R, Ling SH, de Albuquerque VHC, Tavares JMRS. Modified SqueezeNet Architecture for Parkinson's Disease Detection Based on Keypress Data. Biomedicines 2022; 10:2746. [PMID: 36359266 PMCID: PMC9687688 DOI: 10.3390/biomedicines10112746] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/14/2022] [Accepted: 10/21/2022] [Indexed: 08/22/2023] Open
Abstract
Parkinson's disease (PD) is the most common form of Parkinsonism, which is a group of neurological disorders with PD-like motor impairments. The disease affects over 6 million people worldwide and is characterized by motor and non-motor symptoms. The affected person has trouble in controlling movements, which may affect simple daily-life tasks, such as typing on a computer. We propose the application of a modified SqueezeNet convolutional neural network (CNN) for detecting PD based on the subject's key-typing patterns. First, the data are pre-processed using data standardization and the Synthetic Minority Oversampling Technique (SMOTE), and then a Continuous Wavelet Transformation is applied to generate spectrograms used for training and testing a modified SqueezeNet model. The modified SqueezeNet model achieved an accuracy of 90%, representing a noticeable improvement in comparison to other approaches.
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Affiliation(s)
- Lucas Salvador Bernardo
- Department of Software Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania
| | - Robertas Damaševičius
- Department of Software Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania
| | - Sai Ho Ling
- Department of Electrical and Data Engineering, University of Technology Sydney, Sydney 2007, Australia
| | | | - João Manuel R. S. Tavares
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal
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Li Y, Yin Q, Wang B, Shen T, Luo W, Liu T. Preclinical reserpine models recapitulating motor and non-motor features of Parkinson’s disease: Roles of epigenetic upregulation of alpha-synuclein and autophagy impairment. Front Pharmacol 2022; 13:944376. [PMID: 36313295 PMCID: PMC9597253 DOI: 10.3389/fphar.2022.944376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 09/30/2022] [Indexed: 11/13/2022] Open
Abstract
Reserpine is an effective drug for the clinical treatment of hypertension. It also induces Parkinson’s disease (PD)-like symptoms in humans and animals possible through the inhibition of monoamine vesicular transporters, thus decreasing the levels of monoamine neurotransmitters in the brain. However, the precise mechanisms remain unclear. Herein, we aimed to develop a preclinical reserpine model recapitulating the non-motor and motor symptoms of PD and investigate the underlying potential cellular mechanisms. Incubation of reserpine induced apoptosis, led to the accumulation of intracellular reactive oxygen species (ROS), lowered DNA methylation of alpha-synuclein gene, resulted in alpha-synuclein protein deposition, and elevated the ratio of LC3-II/LC3-Ⅰ and p62 in cultured SH-SY5Y cells. Feeding reserpine dose-dependently shortened the lifespan and caused impairment of motor functions in male and female Drosophila. Moreover, long-term oral administration of reserpine led to multiple motor and non-motor symptoms, including constipation, pain hypersensitivity, olfactory impairment, and depression-like behaviors in mice. The mechanistic studies showed that chronic reserpine exposure caused hypomethylation of the alpha-synuclein gene and up-regulated its expression and elevated the ratio of LC3-II/LC3-Ⅰ and expression of p62 in the substantia nigra of mice. Thus, we established preclinical animal models using reserpine to recapitulate the motor and non-motor symptoms of PD. Chronic reserpine exposure epigenetically elevated the levels of alpha-synuclein expression possible by lowering the DNA methylation status and inducing autophagic impairment in vitro and in vivo.
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Affiliation(s)
- Yang Li
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
- Department of Neurology, Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, China
| | - Qiao Yin
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Bing Wang
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Tingting Shen
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Weifeng Luo
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
- *Correspondence: Tong Liu, ; Weifeng Luo,
| | - Tong Liu
- Institute of Pain Medicine and Special Environmental Medicine, Nantong University, Nantong, China
- *Correspondence: Tong Liu, ; Weifeng Luo,
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Patil AD, Biousse V, Newman NJ. Artificial intelligence in ophthalmology: an insight into neurodegenerative disease. Curr Opin Ophthalmol 2022; 33:432-439. [PMID: 35819902 DOI: 10.1097/icu.0000000000000877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW The aging world population accounts for the increasing prevalence of neurodegenerative diseases such as Alzheimer's and Parkinson's which carry a significant health and economic burden. There is therefore a need for sensitive and specific noninvasive biomarkers for early diagnosis and monitoring. Advances in retinal and optic nerve multimodal imaging as well as the development of artificial intelligence deep learning systems (AI-DLS) have heralded a number of promising advances of which ophthalmologists are at the forefront. RECENT FINDINGS The association among retinal vascular, nerve fiber layer, and macular findings in neurodegenerative disease is well established. In order to optimize the use of these ophthalmic parameters as biomarkers, validated AI-DLS are required to ensure clinical efficacy and reliability. Varied image acquisition methods and protocols as well as variability in neurogenerative disease diagnosis compromise the robustness of ground truths that are paramount to developing high-quality training datasets. SUMMARY In order to produce effective AI-DLS for the diagnosis and monitoring of neurodegenerative disease, multicenter international collaboration is required to prospectively produce large inclusive datasets, acquired through standardized methods and protocols. With a uniform approach, the efficacy of resultant clinical applications will be maximized.
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Affiliation(s)
| | | | - Nancy J Newman
- Department of Ophthalmology
- Department of Neurology
- Department of Neurological Surgery, Emory University School of Medicine, Atlanta, Georgia, USA
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Parkinson’s disease diagnosis using neural networks: Survey and comprehensive evaluation. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.102909] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Tanko D, Barua PD, Dogan S, Tuncer T, Palmer E, Ciaccio EJ, Acharya UR. EPSPatNet86: eight-pointed star pattern learning network for detection ADHD disorder using EEG signals. Physiol Meas 2022; 43. [PMID: 35377344 DOI: 10.1088/1361-6579/ac59dc] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 03/01/2022] [Indexed: 12/22/2022]
Abstract
Objective.The main objective of this work is to present a hand-modelled one-dimensional signal classification system to detect Attention-Deficit Hyperactivity Disorder (ADHD) disorder using electroencephalography (EEG) signals.Approach.A novel handcrafted feature extraction method is presented in this research. Our proposed method uses a directed graph and an eight-pointed star pattern (EPSPat). Also, tunable q wavelet transforms (TQWT), wavelet packet decomposition (WPD), statistical extractor, iterative Chi2 (IChi2) selector, and the k-nearest neighbors (kNN) classifier have been utilized to develop the EPSPat based learning model. This network uses two wavelet decomposition methods (TQWT and WPD), and 85 wavelet coefficient bands are extracted. The proposed EPSPat and statistical feature creator generate features from the 85 wavelet coefficient bands and the original EEG signal. The learning network is termed EPSPatNet86. The main purpose of the presented EPSPatNet86 is to detect abnormalities of the EEG signals. Therefore, 85 wavelet subbands have been generated to extract features. The created 86 feature vectors have been evaluated using the Chi2 selector and the kNN classifier in the loss value calculation phase. The final features vector is created by employing a minimum loss-valued eight feature vectors. The IChi2 selector selects the best feature vector, which is fed to the kNN classifier. An EEG signal dataset has been used to demonstrate the presented model's EEG signal classification ability. We have used an ADHD EEG dataset since ADHD is a commonly seen brain-related ailment.Main results.Our developed EPSPatNet86 model can detect the ADHD EEG signals with 97.19% and 87.60% accuracy using 10-fold cross and subject-wise validations, respectively.Significance.The calculated results demonstrate that the presented EPSPatNet86 attained satisfactory EEG classification ability. Results show that we can apply our developed EPSPatNet86 model to other EEG signal datasets to detect abnormalities.
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Affiliation(s)
- Dahiru Tanko
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Australia.,Faculty of Engineering and Information Technology, University of Technology Sydney, Australia.,Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Elizabeth Palmer
- Centre of Clinical Genetics, Sydney Children's Hospitals Network, Randwick 2031, Australia.,School of Women's and Children's Health, University of New South Wales, Randwick 2031, Australia
| | - Edward J Ciaccio
- Department of Medicine, Columbia University Irving Medical Center, United States of America
| | - U Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore.,Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore.,Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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Loh HW, Ooi CP, Barua PD, Palmer EE, Molinari F, Acharya UR. Automated detection of ADHD: Current trends and future perspective. Comput Biol Med 2022; 146:105525. [DOI: 10.1016/j.compbiomed.2022.105525] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/31/2022] [Accepted: 04/04/2022] [Indexed: 12/25/2022]
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Loh HW, Xu S, Faust O, Ooi CP, Barua PD, Chakraborty S, Tan RS, Molinari F, Acharya UR. Application of photoplethysmography signals for healthcare systems: An in-depth review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 216:106677. [PMID: 35139459 DOI: 10.1016/j.cmpb.2022.106677] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/30/2022] [Accepted: 01/30/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVES Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals. METHODS We applied PRISMA guidelines to systematically search various journal databases and identified 43 PPG studies that fit the criteria of this review. RESULTS Twenty-five health issues were identified from these studies that were classified into six categories: cardiac, blood pressure, sleep health, mental health, diabetes, and miscellaneous. Various routes were employed in these PPG studies to perform the diagnosis: machine learning, deep learning, and statistical routes. The studies were reviewed and summarized. CONCLUSIONS We identified limitations such as poor standardization of sampling frequencies and lack of publicly available PPG databases. We urge that future work should consider creating more publicly available databases so that a wide spectrum of health problems can be covered. We also want to promote the use of PPG signals as a potential precision medicine tool in both ambulatory and hospital settings.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Shuting Xu
- Cogninet Australia, Sydney, New South Wales 2010, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Australia
| | - Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, United Kingdom
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Prabal Datta Barua
- Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; School of Business (Information Systems), Faculty of Business, Education, Law and Arts, University of Southern Queensland, Australia
| | - Subrata Chakraborty
- School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia; Centre for Advanced Modelling and Geospatial lnformation Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, 169609, Singapore; Duke-NUS Medical School, 169857, Singapore
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, Italy
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Business (Information Systems), Faculty of Business, Education, Law and Arts, University of Southern Queensland, Australia; School of Engineering, Ngee Ann Polytechnic, 535 Clementi Road, 599489, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan.
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