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Article ; Online: Audio-based Active and Assisted Living: A review of selected applications and future trends.

Despotovic, Vladimir / Pocta, Peter / Zgank, Andrej

2022 Volume 149, Page(s) 106027

Abstract: The development of big data, machine learning, and the Internet of Things has led to rapid advances in the research field of Active and Assisted Living (AAL). A human is placed in the center of such an environment, interacting with different modalities ... ...

Abstract	The development of big data, machine learning, and the Internet of Things has led to rapid advances in the research field of Active and Assisted Living (AAL). A human is placed in the center of such an environment, interacting with different modalities while using the system. Although video still plays a dominant role in AAL technologies, audio, as the most natural means of interaction, is also used commonly, either as a single source of information, or in combination with other modalities. Despite the rapidly increased research efforts in the last decade, there is a lack of systematic overview of audio based technologies and applications in AAL. This review tries to fill this gap, and identifies five major topics where audio is an essential AAL building block: Physiological monitoring, emotion recognition in the context of AAL, human activity recognition, fall detection, and food intake monitoring. We address the data work flow and standard sensing technologies for capturing audio in the AAL environment, provide a comprehensive overview of audio-based AAL applications, and identify datasets available to the research community. Finally, we address the main challenges that should be handled in the upcoming years, and try to identify the potential future trends in audio-based AAL.
MeSH term(s)	Accidental Falls ; Assisted Living Facilities ; Human Activities ; Humans ; Monitoring, Physiologic
Language	English
Publishing date	2022-08-25
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2022.106027
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Article ; Online: Voice for Health: The Use of Vocal Biomarkers from Research to Clinical Practice.

Fagherazzi, Guy / Fischer, Aurélie / Ismael, Muhannad / Despotovic, Vladimir

Digital biomarkers

2021 Volume 5, Issue 1, Page(s) 78–88

Abstract: Diseases can affect organs such as the heart, lungs, brain, muscles, or vocal folds, which can then alter an individual's voice. Therefore, voice analysis using artificial intelligence opens new opportunities for healthcare. From using vocal biomarkers ... ...

Abstract	Diseases can affect organs such as the heart, lungs, brain, muscles, or vocal folds, which can then alter an individual's voice. Therefore, voice analysis using artificial intelligence opens new opportunities for healthcare. From using vocal biomarkers for diagnosis, risk prediction, and remote monitoring of various clinical outcomes and symptoms, we offer in this review an overview of the various applications of voice for health-related purposes. We discuss the potential of this rapidly evolving environment from a research, patient, and clinical perspective. We also discuss the key challenges to overcome in the near future for a substantial and efficient use of voice in healthcare.
Language	English
Publishing date	2021-04-16
Publishing country	Switzerland
Document type	Journal Article ; Review
ISSN	2504-110X
ISSN (online)	2504-110X
DOI	10.1159/000515346
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Article ; Online: Design of a 2-Bit Neural Network Quantizer for Laplacian Source.

Perić, Zoran / Savić, Milan / Simić, Nikola / Denić, Bojan / Despotović, Vladimir

Entropy (Basel, Switzerland)

2021 Volume 23, Issue 8

Abstract: Achieving real-time inference is one of the major issues in contemporary neural network applications, as complex algorithms are frequently being deployed to mobile devices that have constrained storage and computing power. Moving from a full-precision ... ...

Abstract	Achieving real-time inference is one of the major issues in contemporary neural network applications, as complex algorithms are frequently being deployed to mobile devices that have constrained storage and computing power. Moving from a full-precision neural network model to a lower representation by applying quantization techniques is a popular approach to facilitate this issue. Here, we analyze in detail and design a 2-bit uniform quantization model for Laplacian source due to its significance in terms of implementation simplicity, which further leads to a shorter processing time and faster inference. The results show that it is possible to achieve high classification accuracy (more than 96% in the case of MLP and more than 98% in the case of CNN) by implementing the proposed model, which is competitive to the performance of the other quantization solutions with almost optimal precision.
Language	English
Publishing date	2021-07-22
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e23080933
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Article: Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study.

Despotovic, Vladimir / Kim, Sang-Yoon / Hau, Ann-Christin / Kakoichankava, Aliaksandra / Klamminger, Gilbert Georg / Borgmann, Felix Bruno Kleine / Frauenknecht, Katrin B M / Mittelbronn, Michel / Nazarov, Petr V

Heliyon

2024 Volume 10, Issue 5, Page(s) e27515

Abstract: We provide in this paper a comprehensive comparison of various transfer learning strategies and deep learning architectures for computer-aided classification of adult-type diffuse gliomas. We evaluate the generalizability of out-of-domain ImageNet ... ...

Abstract	We provide in this paper a comprehensive comparison of various transfer learning strategies and deep learning architectures for computer-aided classification of adult-type diffuse gliomas. We evaluate the generalizability of out-of-domain ImageNet representations for a target domain of histopathological images, and study the impact of in-domain adaptation using self-supervised and multi-task learning approaches for pretraining the models using the medium-to-large scale datasets of histopathological images. A semi-supervised learning approach is furthermore proposed, where the fine-tuned models are utilized to predict the labels of unannotated regions of the whole slide images (WSI). The models are subsequently retrained using the ground-truth labels and weak labels determined in the previous step, providing superior performance in comparison to standard in-domain transfer learning with balanced accuracy of 96.91% and F1-score 97.07%, and minimizing the pathologist's efforts for annotation. Finally, we provide a visualization tool working at WSI level which generates heatmaps that highlight tumor areas; thus, providing insights to pathologists concerning the most informative parts of the WSI.
Language	English
Publishing date	2024-03-06
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2024.e27515
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Article ; Online: Quantitative SARS-CoV-2 RT-PCR and Bronchoalveolar Cytokine Concentrations Redefine the COVID-19 Phenotypes in Critically Ill Patients.

Vazquez Guillamet, M Cristina / Vazquez Guillamet, Rodrigo / Rjob, Ashraf / Reynolds, Daniel / Parikh, Bijal / Despotovic, Vladimir / Byers, Derek E / Ellebedy, Ali H / Kollef, Marin H / Mudd, Philip A

Journal of intensive care medicine

2024 Volume 39, Issue 6, Page(s) 525–533

Abstract: Rationale: Recent studies suggest that both hypo- and hyperinflammatory acute respiratory distress syndrome (ARDS) phenotypes characterize severe COVID-19-related pneumonia. The role of lung Severe Acute Respiratory Syndrome - Coronavirus 2 (SARS-CoV-2) ...

Abstract	Rationale: Recent studies suggest that both hypo- and hyperinflammatory acute respiratory distress syndrome (ARDS) phenotypes characterize severe COVID-19-related pneumonia. The role of lung Severe Acute Respiratory Syndrome - Coronavirus 2 (SARS-CoV-2) viral load in contributing to these phenotypes remains unknown. Objectives: To redefine COVID-19 ARDS phenotypes when considering quantitative SARS-CoV-2 RT-PCR in the bronchoalveolar lavage of intubated patients. To compare the relevance of deep respiratory samples versus plasma in linking the immune response and the quantitative viral loads. Methods: Eligible subjects were adults diagnosed with COVID-19 ARDS who required mechanical ventilation and underwent bronchoscopy. We recorded the immune response in the bronchoalveolar lavage and plasma and the quantitative SARS-CoV-2 RT-PCR in the bronchoalveolar lavage. Hierarchical clustering on principal components was applied separately on the 2 compartments' datasets. Baseline characteristics were compared between clusters. Measurements and results: Twenty subjects were enrolled between August 2020 and March 2021. Subjects underwent bronchoscopy on average 3.6 days after intubation. All subjects were treated with dexamethasone prior to bronchoscopy, 11 of 20 (55.6%) received remdesivir and 1 of 20 (5%) received tocilizumab. Adding viral load information to the classic 2-cluster model of ARDS revealed a new cluster characterized by hypoinflammatory responses and high viral load in 23.1% of the cohort. Hyperinflammatory ARDS was noted in 15.4% of subjects. Bronchoalveolar lavage clusters were more stable compared to plasma. Conclusions: We identified a unique group of critically ill subjects with COVID-19 ARDS who exhibit hypoinflammatory responses but high viral loads in the lower airways. These clusters may warrant different treatment approaches to improve clinical outcomes.
MeSH term(s)	Humans ; COVID-19/immunology ; COVID-19/diagnosis ; Male ; Female ; Critical Illness ; Middle Aged ; Viral Load ; SARS-CoV-2 ; Bronchoalveolar Lavage Fluid/virology ; Bronchoalveolar Lavage Fluid/chemistry ; Cytokines/analysis ; Cytokines/blood ; Aged ; Phenotype ; Respiration, Artificial ; Respiratory Distress Syndrome/virology ; Bronchoscopy ; Adult ; COVID-19 Nucleic Acid Testing ; Antibodies, Monoclonal, Humanized
Chemical Substances	Cytokines ; tocilizumab (I031V2H011) ; Antibodies, Monoclonal, Humanized
Language	English
Publishing date	2024-04-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	632828-3
ISSN	1525-1489 ; 0885-0666
ISSN (online)	1525-1489
ISSN	0885-0666
DOI	10.1177/08850666231217707
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Article ; Online: Acute chest syndrome from sickle cell disease successfully supported with veno-venous extracorporeal membrane oxygenation.

Grotberg, John C / Sullivan, Mary / McDonald, Rachel K / Despotovic, Vladimir / Witt, Chad A / Reynolds, Daniel / Lee, Janet S / Kotkar, Kunal / Masood, Muhammad F / Kraft, Bryan D / Pawale, Amit

Artificial organs

2024

Language	English
Publishing date	2024-04-22
Publishing country	United States
Document type	Case Reports
ZDB-ID	441812-8
ISSN	1525-1594 ; 0160-564X
ISSN (online)	1525-1594
ISSN	0160-564X
DOI	10.1111/aor.14761
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Article ; Online: Detection of COVID-19 from voice, cough and breathing patterns: Dataset and preliminary results.

Despotovic, Vladimir / Ismael, Muhannad / Cornil, Maël / Call, Roderick Mc / Fagherazzi, Guy

Computers in biology and medicine

2021 Volume 138, Page(s) 104944

Abstract: COVID-19 heavily affects breathing and voice and causes symptoms that make patients' voices distinctive, creating recognizable audio signatures. Initial studies have already suggested the potential of using voice as a screening solution. In this article ... ...

Abstract	COVID-19 heavily affects breathing and voice and causes symptoms that make patients' voices distinctive, creating recognizable audio signatures. Initial studies have already suggested the potential of using voice as a screening solution. In this article we present a dataset of voice, cough and breathing audio recordings collected from individuals infected by SARS-CoV-2 virus, as well as non-infected subjects via large scale crowdsourced campaign. We describe preliminary results for detection of COVID-19 from cough patterns using standard acoustic features sets, wavelet scattering features and deep audio embeddings extracted from low-level feature representations (VGGish and OpenL3). Our models achieve accuracy of 88.52%, sensitivity of 88.75% and specificity of 90.87%, confirming the applicability of audio signatures to identify COVID-19 symptoms. We furthermore provide an in-depth analysis of the most informative acoustic features and try to elucidate the mechanisms that alter the acoustic characteristics of coughs of people with COVID-19.
MeSH term(s)	COVID-19 ; Cough/diagnosis ; Humans ; Respiration ; SARS-CoV-2 ; Voice
Language	English
Publishing date	2021-10-13
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2021.104944
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Article ; Online: Vocal biomarker predicts fatigue in people with COVID-19: results from the prospective Predi-COVID cohort study.

Elbéji, Abir / Zhang, Lu / Higa, Eduardo / Fischer, Aurélie / Despotovic, Vladimir / Nazarov, Petr V / Aguayo, Gloria / Fagherazzi, Guy

BMJ open

2022 Volume 12, Issue 11, Page(s) e062463

Abstract: Objective: To develop a vocal biomarker for fatigue monitoring in people with COVID-19.: Design: Prospective cohort study.: Setting: Predi-COVID data between May 2020 and May 2021.: Participants: A total of 1772 voice recordings were used to ... ...

Abstract	Objective: To develop a vocal biomarker for fatigue monitoring in people with COVID-19. Design: Prospective cohort study. Setting: Predi-COVID data between May 2020 and May 2021. Participants: A total of 1772 voice recordings were used to train an AI-based algorithm to predict fatigue, stratified by gender and smartphone's operating system (Android/iOS). The recordings were collected from 296 participants tracked for 2 weeks following SARS-CoV-2 infection. Primary and secondary outcome measures: Four machine learning algorithms (logistic regression, k-nearest neighbours, support vector machine and soft voting classifier) were used to train and derive the fatigue vocal biomarker. The models were evaluated based on the following metrics: area under the curve (AUC), accuracy, F1-score, precision and recall. The Brier score was also used to evaluate the models' calibrations. Results: The final study population included 56% of women and had a mean (±SD) age of 40 (±13) years. Women were more likely to report fatigue (p<0.001). We developed four models for Android female, Android male, iOS female and iOS male users with a weighted AUC of 86%, 82%, 79%, 85% and a mean Brier Score of 0.15, 0.12, 0.17, 0.12, respectively. The vocal biomarker derived from the prediction models successfully discriminated COVID-19 participants with and without fatigue. Conclusions: This study demonstrates the feasibility of identifying and remotely monitoring fatigue thanks to voice. Vocal biomarkers, digitally integrated into telemedicine technologies, are expected to improve the monitoring of people with COVID-19 or Long-COVID. Trial registration number: NCT04380987.
MeSH term(s)	Humans ; Female ; Male ; Adult ; Middle Aged ; COVID-19/diagnosis ; Prospective Studies ; Cohort Studies ; SARS-CoV-2 ; Biomarkers ; Fatigue/diagnosis ; Fatigue/etiology
Chemical Substances	Biomarkers
Language	English
Publishing date	2022-11-22
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2599832-8
ISSN	2044-6055 ; 2044-6055
ISSN (online)	2044-6055
ISSN	2044-6055
DOI	10.1136/bmjopen-2022-062463
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Article ; Online: A voice-based biomarker for monitoring symptom resolution in adults with COVID-19: Findings from the prospective Predi-COVID cohort study.

Fagherazzi, Guy / Zhang, Lu / Elbéji, Abir / Higa, Eduardo / Despotovic, Vladimir / Ollert, Markus / Aguayo, Gloria A / Nazarov, Petr V / Fischer, Aurélie

PLOS digital health

2022 Volume 1, Issue 10, Page(s) e0000112

Abstract: People with COVID-19 can experience impairing symptoms that require enhanced surveillance. Our objective was to train an artificial intelligence-based model to predict the presence of COVID-19 symptoms and derive a digital vocal biomarker for easily and ... ...

Abstract	People with COVID-19 can experience impairing symptoms that require enhanced surveillance. Our objective was to train an artificial intelligence-based model to predict the presence of COVID-19 symptoms and derive a digital vocal biomarker for easily and quantitatively monitoring symptom resolution. We used data from 272 participants in the prospective Predi-COVID cohort study recruited between May 2020 and May 2021. A total of 6473 voice features were derived from recordings of participants reading a standardized pre-specified text. Models were trained separately for Android devices and iOS devices. A binary outcome (symptomatic versus asymptomatic) was considered, based on a list of 14 frequent COVID-19 related symptoms. A total of 1775 audio recordings were analyzed (6.5 recordings per participant on average), including 1049 corresponding to symptomatic cases and 726 to asymptomatic ones. The best performances were obtained from Support Vector Machine models for both audio formats. We observed an elevated predictive capacity for both Android (AUC = 0.92, balanced accuracy = 0.83) and iOS (AUC = 0.85, balanced accuracy = 0.77) as well as low Brier scores (0.11 and 0.16 respectively for Android and iOS when assessing calibration. The vocal biomarker derived from the predictive models accurately discriminated asymptomatic from symptomatic individuals with COVID-19 (t-test P-values<0.001). In this prospective cohort study, we have demonstrated that using a simple, reproducible task of reading a standardized pre-specified text of 25 seconds enabled us to derive a vocal biomarker for monitoring the resolution of COVID-19 related symptoms with high accuracy and calibration.
Language	English
Publishing date	2022-10-20
Publishing country	United States
Document type	Journal Article
ISSN	2767-3170
ISSN (online)	2767-3170
DOI	10.1371/journal.pdig.0000112
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Article ; Online: Use of Inhaled Epoprostenol in Patients With COVID-19 Receiving Humidified, High-Flow Nasal Oxygen Is Associated With Progressive Respiratory Failure.

Michelson, Andrew P / Lyons, Patrick G / Nguyen, Nguyet M / Reynolds, Daniel / McDonald, Rachel / McEvoy, Colleen A / Despotovic, Vladimir / Brody, Steven L / Kollef, Marin H / Kraft, Bryan D

CHEST critical care

2023 Volume 1, Issue 3

Abstract: Background: The clinical benefit of using inhaled epoprostenol (iEpo) through a humidified high-flow nasal cannula (HHFNC) remains unknown for patients with COVID-19.: Research question: Can iEpo prevent respiratory deterioration for patients with ... ...

Abstract	Background: The clinical benefit of using inhaled epoprostenol (iEpo) through a humidified high-flow nasal cannula (HHFNC) remains unknown for patients with COVID-19. Research question: Can iEpo prevent respiratory deterioration for patients with positive SARS-CoV-2 findings receiving HHFNC? Study design and methods: This multicenter retrospective cohort analysis included patients aged 18 years or older with COVID-19 pneumonia who required HHFNC treatment. Patients who received iEpo were propensity score matched to patients who did not receive iEpo. The primary outcome was time to mechanical ventilation or death without mechanical ventilation and was assessed using Kaplan-Meier curves and Cox proportional hazard ratios. The effects of residual confounding were assessed using a multilevel analysis, and a secondary analysis adjusted for outcome propensity also was performed in a multivariable model that included the entire (unmatched) patient cohort. Results: Among 954 patients with positive SARS-CoV-2 findings receiving HHFNC therapy, 133 patients (13.9%) received iEpo. After propensity score matching, the median number of days until the composite outcome was similar between treatment groups (iEpo: 5.0 days [interquartile range, 2.0-10.0 days] vs no-iEpo: 6.5 days [interquartile range, 2.0-11.0 days]; Interpretation: In patients with COVID-19 receiving HHFNC therapy, use of iEpo was associated with the need for invasive mechanical ventilation.
Language	English
Publishing date	2023-09-25
Publishing country	United States
Document type	Journal Article
ISSN	2949-7884
ISSN (online)	2949-7884
DOI	10.1016/j.chstcc.2023.100019
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