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Mohammadi Moghadam S, Ortega Auriol P, Yeung T, Choisne J. 3D gait analysis in children using wearable sensors: feasibility of predicting joint kinematics and kinetics with personalized machine learning models and inertial measurement units. Front Bioeng Biotechnol 2024; 12:1372669. [PMID: 38572359 PMCID: PMC10987962 DOI: 10.3389/fbioe.2024.1372669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 03/06/2024] [Indexed: 04/05/2024] Open
Abstract
Introduction: Children's walking patterns evolve with age, exhibiting less repetitiveness at a young age and more variability than adults. Three-dimensional gait analysis (3DGA) is crucial for understanding and treating lower limb movement disorders in children, traditionally performed using Optical Motion Capture (OMC). Inertial Measurement Units (IMUs) offer a cost-effective alternative to OMC, although challenges like drift errors persist. Machine learning (ML) models can mitigate these issues in adults, prompting an investigation into their applicability to a heterogeneous pediatric population. This study aimed at 1) quantifying personalized and generalized ML models' performance for predicting gait time series in typically developed (TD) children using IMUs data, 2) Comparing random forest (RF) and convolutional neural networks (CNN) models' performance, 3) Finding the optimal number of IMUs required for accurate predictions. Methodology: Seventeen TD children, aged 6 to 15, participated in data collection involving OMC, force plates, and IMU sensors. Joint kinematics and kinetics (targets) were computed from OMC and force plates' data using OpenSim. Tsfresh, a Python package, extracted features from raw IMU data. Each target's ten most important features were input in the development of personalized and generalized RF and CNN models. This procedure was initially conducted with 7 IMUs placed on all lower limb segments and then performed using only two IMUs on the feet. Results: Findings suggested that the RF and CNN models demonstrated comparable performance. RF predicted joint kinematics with a 9.5% and 19.9% NRMSE for personalized and generalized models, respectively, and joint kinetics with an NRMSE of 10.7% for personalized and 15.2% for generalized models in TD children. Personalized models provided accurate estimations from IMU data in children, while generalized models lacked accuracy due to the limited dataset. Furthermore, reducing the number of IMUs from 7 to 2 did not affect the results, and the performance remained consistent. Discussion: This study proposed a promising personalized approach for gait time series prediction in children, involving an RF model and two IMUs on the feet.
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Affiliation(s)
| | | | | | - Julie Choisne
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
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2
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Tsagiopoulou M, Gut IG. Machine learning and multi-omics data in chronic lymphocytic leukemia: the future of precision medicine? Front Genet 2024; 14:1304661. [PMID: 38283149 PMCID: PMC10811210 DOI: 10.3389/fgene.2023.1304661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/27/2023] [Indexed: 01/30/2024] Open
Abstract
Chronic lymphocytic leukemia is a complex and heterogeneous hematological malignancy. The advance of high-throughput multi-omics technologies has significantly influenced chronic lymphocytic leukemia research and paved the way for precision medicine approaches. In this review, we explore the role of machine learning in the analysis of multi-omics data in this hematological malignancy. We discuss recent literature on different machine learning models applied to single omic studies in chronic lymphocytic leukemia, with a special focus on the potential contributions to precision medicine. Finally, we highlight the recently published machine learning applications in multi-omics data in this area of research as well as their potential and limitations.
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Affiliation(s)
| | - Ivo G. Gut
- Centro Nacional de Analisis Genomico (CNAG), Barcelona, Spain
- Universitat de Barcelona (UB), Barcelona, Spain
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3
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Akdeniz M, Al-Shaebi Z, Altunbek M, Bayraktar C, Kayabolen A, Bagci-Onder T, Aydin O. Characterization and discrimination of spike protein in SARS-CoV-2 virus-like particles via surface-enhanced Raman spectroscopy. Biotechnol J 2024; 19:e2300191. [PMID: 37750467 DOI: 10.1002/biot.202300191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 09/11/2023] [Accepted: 09/22/2023] [Indexed: 09/27/2023]
Abstract
Non-infectious virus-like particles (VLPs) are excellent structures for development of many biomedical applications such as drug delivery systems, vaccine production platforms, and detection techniques for infectious diseases including SARS-CoV-2 VLPs. The characterization of biochemical and biophysical properties of purified VLPs is crucial for development of detection methods and therapeutics. The presence of spike (S) protein in their structure is especially important since S protein induces immunological response. In this study, development of a rapid, low-cost, and easy-to-use technique for both characterization and detection of S protein in the two VLPs, which are SARS-CoV-2 VLPs and HIV-based VLPs was achieved using surface-enhanced Raman spectroscopy (SERS). To analyze and classify datasets of SERS spectra obtained from the VLP groups, machine learning classification techniques including support vector machine (SVM), k-nearest neighbors (kNN), and random forest (RF) were utilized. Among them, the SVM classification algorithm demonstrated the best classification performance for SARS-CoV-2 VLPs and HIV-based VLPs groups with 87.5% and 92.5% accuracy, respectively. This study could be valuable for the rapid characterization of VLPs for the development of novel therapeutics or detection of structural proteins of viruses leading to a variety of infectious diseases.
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Affiliation(s)
- Munevver Akdeniz
- Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey
- Nanothera Lab, Drug Application and Research Center (ERFARMA), Erciyes University, Kayseri, Turkey
| | - Zakarya Al-Shaebi
- Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey
- Nanothera Lab, Drug Application and Research Center (ERFARMA), Erciyes University, Kayseri, Turkey
| | - Mine Altunbek
- Department of Chemical Engineering, University of Massachusetts, Lowell, Massachusetts, USA
| | - Canan Bayraktar
- Koç University Research Center for Translational Medicine (KUTTAM), Koç University, Istanbul, Turkey
| | - Alisan Kayabolen
- Koç University Research Center for Translational Medicine (KUTTAM), Koç University, Istanbul, Turkey
- McGovern Institute for Brain Research at MIT, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Tugba Bagci-Onder
- Koç University Research Center for Translational Medicine (KUTTAM), Koç University, Istanbul, Turkey
| | - Omer Aydin
- Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey
- Nanothera Lab, Drug Application and Research Center (ERFARMA), Erciyes University, Kayseri, Turkey
- Clinical Engineering Research and Implementation Center (ERKAM), Erciyes University, Kayseri, Turkey
- Nanotechnology Research and Application Center (ERNAM), Erciyes University, Kayseri, Turkey
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Katoch V, Kumar A, Imam F, Sarkar D, Knibbs LD, Liu Y, Ganguly D, Dey S. Addressing Biases in Ambient PM 2.5 Exposure and Associated Health Burden Estimates by Filling Satellite AOD Retrieval Gaps over India. Environ Sci Technol 2023; 57:19190-19201. [PMID: 37956255 DOI: 10.1021/acs.est.3c03355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Ambient PM2.5 exposure statistics in countries with limited ground monitors are derived from satellite aerosol optical depth (AOD) products that have spatial gaps. Here, we quantified the biases in PM2.5 exposure and associated health burden in India due to the sampling gaps in AOD retrieved by a Moderate Resolution Imaging Spectroradiometer. We filled the sampling gaps and derived PM2.5 in recent years (2017-2022) over India, which showed fivefold cross-validation R2 of 0.92 and root mean square error (RMSE) of 11.8 μg m-3 on an annual scale against ground-based measurements. If the missing AOD values are not accounted for, the exposure would be overestimated by 19.1%, translating to an overestimation in the mortality burden by 93,986 (95% confidence interval: 78,638-110,597) during these years. With the gap-filled data, we found that the rising ambient PM2.5 trend in India has started showing a sign of stabilization in recent years. However, a reduction in population-weighted exposure balanced out the effect of the increasing population and maintained the mortality burden attributable to ambient PM2.5 for 2022 (991,058:798,220-1,183,896) comparable to the 2017 level (1,014,766:812,186-1,217,346). Therefore, a decline in exposure alone is not sufficient to significantly reduce the health burden attributable to ambient PM2.5 in India.
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Affiliation(s)
- Varun Katoch
- Centre for Atmospheric Sciences, IIT, New Delhi, Delhi 110016, India
| | - Alok Kumar
- Centre for Atmospheric Sciences, IIT, New Delhi, Delhi 110016, India
| | - Fahad Imam
- Centre for Atmospheric Sciences, IIT, New Delhi, Delhi 110016, India
| | - Debajit Sarkar
- Centre for Atmospheric Sciences, IIT, New Delhi, Delhi 110016, India
| | - Luke D Knibbs
- School of Public Health, The University of Sydney, Camperdown, New South Wales 2006, Australia
- Public Health Research Analytics and Methods for Evidence, Public Health Unit, Sydney Local Health District, Camperdown, NSW 2050, Australia
| | - Yang Liu
- Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
| | - Dilip Ganguly
- Centre for Atmospheric Sciences, IIT, New Delhi, Delhi 110016, India
| | - Sagnik Dey
- Centre for Atmospheric Sciences, IIT, New Delhi, Delhi 110016, India
- Centre of Excellence for Research on Clean Air, IIT, New Delhi, Delhi 110016, India
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Pelzer L, Schulze T, Buschmann D, Enslin C, Schmitt R, Hopmann C. Acquiring Process Knowledge in Extrusion-Based Additive Manufacturing via Interpretable Machine Learning. Polymers (Basel) 2023; 15:3509. [PMID: 37688135 PMCID: PMC10490136 DOI: 10.3390/polym15173509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 08/18/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023] Open
Abstract
Additive manufacturing (AM), especially the extrusion-based process, has many process parameters which influence the resulting part properties. Those parameters have complex interdependencies and are therefore difficult if not impossible to model analytically. Machine learning (ML) is a promising approach to find suitable combinations of process parameters for manufacturing a part with desired properties without having to analytically model the process in its entirety. However, ML-based approaches are typically black box models. Therefore, it is difficult to verify their output and to derive process knowledge from such approaches. This study uses interpretable machine learning methods to derive process knowledge from interpreted data sets by analyzing the model's feature importance. Using fused layer modeling (FLM) as an exemplary manufacturing technology, it is shown that the process can be characterized entirely. Therefore, sweet spots for process parameters can be determined objectively. Additionally, interactions between parameters are discovered, and the basis for further investigations is established.
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Affiliation(s)
- Lukas Pelzer
- Institute for Plastics Processing at RWTH Aachen University, 52074 Aachen, Germany
| | - Tobias Schulze
- Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, 52074 Aachen, Germany; (T.S.); (D.B.)
| | - Daniel Buschmann
- Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, 52074 Aachen, Germany; (T.S.); (D.B.)
| | - Chrismarie Enslin
- Cybernetics Lab IMA & IfU, RWTH Aachen University, 52068 Aachen, Germany
| | - Robert Schmitt
- Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, 52074 Aachen, Germany; (T.S.); (D.B.)
| | - Christian Hopmann
- Institute for Plastics Processing at RWTH Aachen University, 52074 Aachen, Germany
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Stephens DC, Crabtree A, Beasley HK, Garza-Lopez E, Mungai M, Vang L, Neikirk K, Vue Z, Vue N, Marshall AG, Turner K, Shao JQ, Sarker B, Murray S, Gaddy JA, Hinton AO, Damo S, Davis J. In the Age of Machine Learning Cryo-EM Research is Still Necessary: A Path toward Precision Medicine. Adv Biol (Weinh) 2023; 7:e2300122. [PMID: 37246245 DOI: 10.1002/adbi.202300122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/29/2023] [Indexed: 05/30/2023]
Abstract
Machine learning has proven useful in analyzing complex biological data and has greatly influenced the course of research in structural biology and precision medicine. Deep neural network models oftentimes fail to predict the structure of complex proteins and are heavily dependent on experimentally determined structures for their training and validation. Single-particle cryogenic electron microscopy (cryoEM) is also advancing the understanding of biology and will be needed to complement these models by continuously supplying high-quality experimentally validated structures for improvements in prediction quality. In this perspective, the significance of structure prediction methods is highlighted, but the authors also ask, what if these programs cannot accurately predict a protein structure important for preventing disease? The role of cryoEM is discussed to help fill the gaps left by artificial intelligence predictive models in resolving targetable proteins and protein complexes that will pave the way for personalized therapeutics.
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Affiliation(s)
- Dominique C Stephens
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
- Department of Life and Physical Sciences, Fisk University, Nashville, TN, 37232, USA
| | - Amber Crabtree
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Heather K Beasley
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Edgar Garza-Lopez
- Department of Internal Medicine, University of Iowa, Iowa City, IA, 52242, USA
| | - Margaret Mungai
- Department of Internal Medicine, University of Iowa, Iowa City, IA, 52242, USA
| | - Larry Vang
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Kit Neikirk
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Zer Vue
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Neng Vue
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Andrea G Marshall
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Kyrin Turner
- Department of Life and Physical Sciences, Fisk University, Nashville, TN, 37232, USA
| | - Jian-Qiang Shao
- Central Microscopy Research Facility, University of Iowa, Iowa City, IA, 52242, USA
| | - Bishnu Sarker
- School of Applied Computational Sciences, Meharry Medical College, Nashville, TN, 37208, USA
| | - Sandra Murray
- Department of Cell Biology, College of Medicine, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Jennifer A Gaddy
- Division of Infectious Diseases, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA
- U.S. Department of Veterans Affairs, Tennessee Valley Healthcare Systems, Nashville, TN, 37212, USA
| | - Antentor O Hinton
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Steven Damo
- Department of Life and Physical Sciences, Fisk University, Nashville, TN, 37232, USA
| | - Jamaine Davis
- Department of Biochemistry and Cancer Biology, Meharry Medical College, Nashville, TN, 37208, USA
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Karlsen ST, Rau MH, Sánchez BJ, Jensen K, Zeidan AA. From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry. FEMS Microbiol Rev 2023; 47:fuad030. [PMID: 37286882 PMCID: PMC10337747 DOI: 10.1093/femsre/fuad030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/31/2023] [Accepted: 06/06/2023] [Indexed: 06/09/2023] Open
Abstract
When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.
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Affiliation(s)
- Signe T Karlsen
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Martin H Rau
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Benjamín J Sánchez
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Kristian Jensen
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Ahmad A Zeidan
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
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Tan C, Li Q, Yao X, Chen L, Su J, Ng FL, Liu Y, Yang T, Chew Y, Liu CT, DebRoy T. Machine Learning Customized Novel Material for Energy-Efficient 4D Printing. Adv Sci (Weinh) 2023; 10:e2206607. [PMID: 36739604 PMCID: PMC10074080 DOI: 10.1002/advs.202206607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 01/20/2023] [Indexed: 06/18/2023]
Abstract
Existing commercial powders for laser additive manufacturing (LAM) are designed for traditional manufacturing methods requiring post heat treatments (PHT). LAM's unique cyclic thermal history induces intrinsic heat treatment (IHT) on materials during deposition, which offers an opportunity to develop LAM-customized new materials. This work customized a novel Fe-Ni-Ti-Al maraging steel assisted by machine learning to leverage the IHT effect for in situ forming massive precipitates during LAM without PHT. Fast precipitation kinetics in steel, tailored intermittent deposition strategy, and the IHT effect facilitate the in situ Ni3 Ti precipitation in the martensitic matrix via heterogeneous nucleation on high-density dislocations. The as-built steel achieves a tensile strength of 1538 MPa and a uniform elongation of 8.1%, which is superior to a wide range of as-LAM-processed high-strength steel. In the current mainstream ex situ 4D printing, the time-dependent evolutions (i.e., property or functionality changes) of a 3D printed structure occur after part formation. This work highlights in situ 4D printing via the synchronous integration of time-dependent precipitation hardening with 3D geometry shaping, which shows high energy efficiency and sustainability. The findings provide insight into developing LAM-customized materials by understanding and utilizing the IHT-materials interaction.
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Affiliation(s)
- Chaolin Tan
- Singapore Institute of Manufacturing TechnologyAgency for Science, Technology and Research (A*STAR)2 Fusionopolis WaySingapore138634Singapore
| | - Qian Li
- Department of Materials Science & EngineeringCity University of Hong KongHong Kong SARChina
| | - Xiling Yao
- Singapore Institute of Manufacturing TechnologyAgency for Science, Technology and Research (A*STAR)2 Fusionopolis WaySingapore138634Singapore
| | - Lequn Chen
- Singapore Institute of Manufacturing TechnologyAgency for Science, Technology and Research (A*STAR)2 Fusionopolis WaySingapore138634Singapore
| | - Jinlong Su
- Singapore Institute of Manufacturing TechnologyAgency for Science, Technology and Research (A*STAR)2 Fusionopolis WaySingapore138634Singapore
| | - Fern Lan Ng
- Singapore Institute of Manufacturing TechnologyAgency for Science, Technology and Research (A*STAR)2 Fusionopolis WaySingapore138634Singapore
| | - Yuchan Liu
- Singapore Institute of Manufacturing TechnologyAgency for Science, Technology and Research (A*STAR)2 Fusionopolis WaySingapore138634Singapore
| | - Tao Yang
- Department of Materials Science & EngineeringCity University of Hong KongHong Kong SARChina
| | - Youxiang Chew
- Singapore Institute of Manufacturing TechnologyAgency for Science, Technology and Research (A*STAR)2 Fusionopolis WaySingapore138634Singapore
| | - Chain Tsuan Liu
- Department of Materials Science & EngineeringCity University of Hong KongHong Kong SARChina
| | - Tarasankar DebRoy
- Department of Materials Science & EngineeringPennsylvania State UniversityUniversity ParkPA 16802United States
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Ishrak MS, Cai F, Islam SMM, Borić-Lubecke O, Wu T, Lubecke VM. Doppler radar remote sensing of respiratory function. Front Physiol 2023; 14:1130478. [PMID: 37179837 PMCID: PMC10172641 DOI: 10.3389/fphys.2023.1130478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 04/05/2023] [Indexed: 05/15/2023] Open
Abstract
Doppler radar remote sensing of torso kinematics can provide an indirect measure of cardiopulmonary function. Motion at the human body surface due to heart and lung activity has been successfully used to characterize such measures as respiratory rate and depth, obstructive sleep apnea, and even the identity of an individual subject. For a sedentary subject, Doppler radar can track the periodic motion of the portion of the body moving as a result of the respiratory cycle as distinct from other extraneous motions that may occur, to provide a spatial temporal displacement pattern that can be combined with a mathematical model to indirectly assess quantities such as tidal volume, and paradoxical breathing. Furthermore, it has been demonstrated that even healthy respiratory function results in distinct motion patterns between individuals that vary as a function of relative time and depth measures over the body surface during the inhalation/exhalation cycle. Potentially, the biomechanics that results in different measurements between individuals can be further exploited to recognize pathology related to lung ventilation heterogeneity and other respiratory diagnostics.
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Affiliation(s)
- Mohammad Shadman Ishrak
- Department of Electrical and Computer Engineering, University of Hawaii at Manoa, Honolulu, HI, United States
- *Correspondence: Mohammad Shadman Ishrak,
| | - Fulin Cai
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States
- ASU-Mayo Center for Innovative Imaging, Arizona State University, Tempe, AZ, United States
| | | | - Olga Borić-Lubecke
- Department of Electrical and Computer Engineering, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Teresa Wu
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States
- ASU-Mayo Center for Innovative Imaging, Arizona State University, Tempe, AZ, United States
| | - Victor M. Lubecke
- Department of Electrical and Computer Engineering, University of Hawaii at Manoa, Honolulu, HI, United States
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Schulte T, Bohnet-Joschko S. How can Big Data Analytics Support People-Centred and Integrated Health Services: A Scoping Review. Int J Integr Care 2022; 22:23. [PMID: 35756337 DOI: 10.5334/ijic.5543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 06/08/2022] [Indexed: 01/16/2023] Open
Abstract
Introduction: Health systems in high-income countries face a variety of challenges calling for a systemic approach to improve quality and efficiency. Putting people in the centre is the main idea of the WHO model of people-centred and integrated health services. Integrating health services is fuelled by an integration of health data with great potentials for decision support based on big data analytics. The research question of this paper is “How can big data analytics support people-centred and integrated health services?” Methods: A scoping review following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses – Scoping Review (PRISMA-ScR) statement was conducted to gather information on how big data analytics can support people-centred and integrated health services. The results were summarized in a role model of a people-centred and integrated health services platform illustrating which data sources might be integrated and which types of analytics might be applied to support the strategies of the people-centred and integrated health services framework to become more integrated across the continuum of care. Additional rapid literature reviews were conducted to generate frequency distributions of the most often used data types and analytical methods in the medical literature. Finally, the main challenges connected with big data analytics were worked out based on a content analysis of the results from the scoping literature review. Results: Based on the results from the rapid literature reviews the most often used data sources for big data analytics (BDA) in healthcare were biomarkers (39.3%) and medical images (30.9%). The most often used analytical models were support vector machines (27.3%) and neural networks (20.4%). The people-centred and integrated health services framework defines different strategic interventions for health services to become more integrated. To support all aspects of these interventions a comparably integrated platform of health-related data would be needed, so that a role model labelled as people-centred health platform was developed. Based on integrated data the results of the scoping review (n = 72) indicate, that big data analytics could for example support the strategic intervention of tailoring personalized health plans (43.1%), e.g. by predicting individual risk factors for different therapy options. Also BDA might enhance clinical decision support tools (31.9%), e.g. by calculating risk factors for disease uptake or progression. BDA might also assist in designing population-based services (26.4% by clustering comparable individuals in manageable risk groups e.g. mentored by specifically trained, non-medical professionals. The main challenges of big data analytics in healthcare were categorized in regulatory, (information-) technological, methodological, and cultural issues, whereas methodological challenges were mentioned most often (55.0%), followed by regulatory challenges (43.7%). Discussion: The BDA applications presented in this literature review are based on findings which have already been published. For some important components of the framework on people-centred care like enhancing the role of community care or establishing intersectoral partnerships between health and social care institutions only few examples of enabling big data analytical tools were found in the literature. Quite the opposite does this mean that these strategies have less potential value, but rather that the source systems in these fields need to be further developed to be suitable for big data analytics. Conclusions: Big data analytics can support people-centred and integrated health services e.g. by patient similarity stratifications or predictions of individual risk factors. But BDA fails to unfold its full potential until data source systems are still disconnected and actions towards a comprehensive and people-centred health-related data platform are politically insufficiently incentivized. This work highlighted the potential of big data analysis in the context of the model of people-centred and integrated health services, whereby the role model of the person-centered health platform can be used as a blueprint to support strategies to improve person-centered health care. Likely because health data is extremely sensitive and complex, there are only few practical examples of platforms to some extent already capable of merging and processing people-centred big data, but the integration of health data can be expected to further proceed so that analytical opportunities might also become reality in the near future.
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Katmah R, Al-Shargie F, Tariq U, Babiloni F, Al-Mughairbi F, Al-Nashash H. A Review on Mental Stress Assessment Methods Using EEG Signals. Sensors (Basel) 2021; 21:5043. [PMID: 34372280 PMCID: PMC8347831 DOI: 10.3390/s21155043] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 07/13/2021] [Accepted: 07/19/2021] [Indexed: 01/19/2023]
Abstract
Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contains rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Accordingly, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.
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Affiliation(s)
- Rateb Katmah
- Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah 26666, United Arab Emirates;
| | - Fares Al-Shargie
- Department of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates; (U.T.); (H.A.-N.)
| | - Usman Tariq
- Department of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates; (U.T.); (H.A.-N.)
| | - Fabio Babiloni
- Department of Molecular Medicine, University of Sapienza Rome, 00185 Rome, Italy;
- College Computer Science and Technology, University Hangzhou Dianzi, Hangzhou 310018, China
| | - Fadwa Al-Mughairbi
- College of Medicines and Health Sciences, United Arab Emirates University, Al-Ain 15551, United Arab Emirates;
| | - Hasan Al-Nashash
- Department of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates; (U.T.); (H.A.-N.)
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Lai JW, Ang CKE, Acharya UR, Cheong KH. Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification. Int J Environ Res Public Health 2021; 18:6099. [PMID: 34198829 PMCID: PMC8201065 DOI: 10.3390/ijerph18116099] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 05/26/2021] [Accepted: 05/28/2021] [Indexed: 02/07/2023]
Abstract
Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.
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Affiliation(s)
- Joel Weijia Lai
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
| | - Candice Ke En Ang
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
- MOH Holdings Pte Ltd, 1 Maritime Square, Singapore 099253, Singapore
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore;
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Clementi 599491, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| | - Kang Hao Cheong
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
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Chapman J, Orrell-Trigg R, Kwoon KY, Truong VK, Cozzolino D. A high-throughput and machine learning resistance monitoring system to determine the point of resistance for Escherichia coli with tetracycline: Combining UV-visible spectrophotometry with principal component analysis. Biotechnol Bioeng 2021; 118:1511-1519. [PMID: 33399220 DOI: 10.1002/bit.27664] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 12/13/2020] [Accepted: 12/29/2020] [Indexed: 12/11/2022]
Abstract
UV-visible spectroscopy (UV-Vis) is routinely used in microbiology as a tool to check the optical density (OD) pertaining to the growth stages of microbial cultures at the single wavelength of 600 nm, better known as the OD600 . Typically, modern UV-Vis spectrophotometers can scan in the region of approximately 200-1000 nm in the electromagnetic spectrum, where users do not extend the use of the instrument's full capability in a laboratory. In this study, the full potential of UV-Vis spectrophotometry (multiwavelength collection) was used to examine bacterial growth phases when treated with antibiotics showcasing the ability to understand the point of resistance when an antibiotic is introduced into the media and therefore understand the biochemical changes of the infectious pathogens. A multiplate reader demonstrated a high throughput experiment (96 samples) to understand the growth of Escherichia coli when varied concentrations of the antibiotic tetracycline was added into the well plates. Principal component analysis (PCA) and partial least squares discriminant analysis were then used as the data mining techniques to interpret the UV-Vis spectral data and generate machine learning "proof of principle" for the UV-Vis spectrophotometer plate reader. Results from this study showed that the PCA analysis provides an accurate yet simple visual classification and the recognition of E. coli samples belonging to each treatment. These data show significant advantages when compared to the traditional OD600 method where we can now understand biochemical changes in the system rather than a mere optical density measurement. Due to the unique experimental setup and procedure that involves indirect use of antibiotics, the same test could be used for obtaining practical information on the type, resistance, and dose of antibiotic necessary to establish the optimum diagnosis, treatment, and decontamination strategies for pathogenic and antibiotic resistant species.
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Affiliation(s)
- James Chapman
- Department of Applied Chemistry and Environmental Science, School of Science, RMIT University, Melbourne, Victoria, Australia
| | - Rebecca Orrell-Trigg
- Department of Applied Chemistry and Environmental Science, School of Science, RMIT University, Melbourne, Victoria, Australia
| | - Ki Y Kwoon
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, USA
| | - Vi K Truong
- Department of Applied Chemistry and Environmental Science, School of Science, RMIT University, Melbourne, Victoria, Australia.,Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, USA
| | - Daniel Cozzolino
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, Queensland, Australia
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Tran WT, Suraweera H, Quiaoit K, DiCenzo D, Fatima K, Jang D, Bhardwaj D, Kolios C, Karam I, Poon I, Sannachi L, Gangeh M, Sadeghi-Naini A, Dasgupta A, Czarnota GJ. Quantitative ultrasound delta-radiomics during radiotherapy for monitoring treatment responses in head and neck malignancies. Future Sci OA 2020; 6:FSO624. [PMID: 33235811 DOI: 10.2144/fsoa-2020-0073] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Aim: We investigated quantitative ultrasound (QUS) in patients with node-positive head and neck malignancies for monitoring responses to radical radiotherapy (RT). Materials & methods: QUS spectral and texture parameters were acquired from metastatic lymph nodes 24 h, 1 and 4 weeks after starting RT. K-nearest neighbor and naive-Bayes machine-learning classifiers were used to build prediction models for each time point. Response was detected after 3 months of RT, and patients were classified into complete and partial responders. Results: Single-feature naive-Bayes classification performed best with a prediction accuracy of 80, 86 and 85% at 24 h, week 1 and 4, respectively. Conclusion: QUS-radiomics can predict RT response at 3 months as early as 24 h with reasonable accuracy, which further improves into 1 week of treatment. Patients with head and neck cancer are often treated with radiation, which usually spans over 6–7 weeks. The response is usually measured 3 months after treatment completion. In this study, we had performed ultrasound scans from the patient’s neck node during radiation treatment (after 24 h, 1 and 4 weeks). Artificial intelligence was used to interpret the ultrasound imaging and predict the response to radiation at the end of 3 months. The scans obtained after the first week were able to predict the treatment response with reasonable accuracy (86%).
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Bhardwaj P, Yadav RK, Kurian S. Digitizing the Pharma Neurons - A Technological Operation in Progress! Rev Recent Clin Trials 2020; 15:178-187. [PMID: 32564760 DOI: 10.2174/1574887115666200621183459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 04/27/2020] [Accepted: 05/22/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Digitization and automation are the buzzwords in clinical research and pharma companies are investigating heavily here. Right from drug discovery to personalized medicine, digital patients and patient engagement, there is great consideration of technology at each step. METHODS The published data and online information available is reviewed to give an overview of digitization in pharma, across the drug development cycle, industry collaborations and innovations. The regulatory guidelines, innovative collaborations across industry, academics and thought leadership are presented. Also included are some ideas, suggestions, way forwards while digitizing the pharma neurons, the regulatory stand, benefits and challenges. RESULTS The innovations range from discovering personalized medicine to conducting virtual clinical trials, and maximizing data collection from the real-world experience. To address the increasing demand for the real-world data and the needs of tech-savvy patients, the innovations are shaping up accordingly. Pharma companies are collaborating with academics and they are co-innovating the technology for example Massachusetts Institute of Technology's program. This focuses on the modernization of clinical trials, strategic use of artificial intelligence and machine learning using real-world evidence, assess the risk-benefit ratio of deploying digital analytics in medicine, and proactively identifying the solutions. CONCLUSION With unfolding data on the impact of science and technology amalgamation, we need shared mindset between data scientists and medical professionals to maximize the utility of enormous health and medical data. To tackle this efficiently, there is a need of cross-collaboration and education, and align with ethical and regulatory requirements. A perfect blend of industry, regulatory, and academia will ensure successful digitization of pharma neurons.
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Affiliation(s)
| | - Raj Kumar Yadav
- Integral Health Clinic, Department of Physiology, All India Institute of Medical Sciences, New Delhi-110029, India
| | - Sojan Kurian
- Tata Consultancy Services, New York, NY, United States
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Baghdadi A, Hoshyarmanesh H, de Lotbiniere-Bassett MP, Choi SK, Lama S, Sutherland GR. Data analytics interrogates robotic surgical performance using a microsurgery-specific haptic device. Expert Rev Med Devices 2020; 17:721-730. [PMID: 32536224 DOI: 10.1080/17434440.2020.1782736] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
OBJECTIVES With the increase in robot-assisted cases, recording the quantifiable dexterity of surgeons is essential for proficiency evaluations. The present study employs sensor-based kinematics and recorded surgeon experience for evaluating a new haptic device. METHODS Thirty surgeons performed a task simulating micromanipulation with neuroArmPLUSHD and two commercially available hand-controllers. The surgical performance was evaluated based on subjective measures obtained from survey and objective features derived from the sensors. Statistical analyses were performed to assess the hand-controllers and regression analysis was used to identify the key features and develop a machine learning model for surgical skill assessment. FINDINGS MANCOVA tests on objective features demonstrated significance (α = 0.05) for time (p = 0.02), errors (p = 0.01), distance (p = 0.03), clutch incidents (p = 0.03), and forces (p = 0.00). The majority of metrics were in favor of neuroArmPLUSHD. The surgeons found it smoother, more comfortable, less tiring, and easier to maneuver with more realistic force feedback. The ensemble machine learning model trained with 5-fold cross-validation showed an accuracy (SD) of 0.78 (0.15) in surgeon skill classification. CONCLUSIONS This study validates the importance of incorporating a superior haptic device in telerobotic surgery for standardization of surgical education and patient care.
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Affiliation(s)
- Amir Baghdadi
- Project neuroArm, Department of Clinical Neurosciences, and Hotchkiss Brain Institute, University of Calgary , Calgary, Alberta, Canada
| | - Hamidreza Hoshyarmanesh
- Project neuroArm, Department of Clinical Neurosciences, and Hotchkiss Brain Institute, University of Calgary , Calgary, Alberta, Canada
| | - Madeleine P de Lotbiniere-Bassett
- Project neuroArm, Department of Clinical Neurosciences, and Hotchkiss Brain Institute, University of Calgary , Calgary, Alberta, Canada
| | - Seok Keon Choi
- Project neuroArm, Department of Clinical Neurosciences, and Hotchkiss Brain Institute, University of Calgary , Calgary, Alberta, Canada
| | - Sanju Lama
- Project neuroArm, Department of Clinical Neurosciences, and Hotchkiss Brain Institute, University of Calgary , Calgary, Alberta, Canada
| | - Garnette R Sutherland
- Project neuroArm, Department of Clinical Neurosciences, and Hotchkiss Brain Institute, University of Calgary , Calgary, Alberta, Canada
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Pierleoni P, Belli A, Palma L, Sabbatini L. A Versatile Machine Vision Algorithm for Real-Time Counting Manually Assembled Pieces. J Imaging 2020; 6:48. [PMID: 34460594 DOI: 10.3390/jimaging6060048] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 06/03/2020] [Accepted: 06/09/2020] [Indexed: 12/03/2022] Open
Abstract
The Industry 4.0 paradigm is based on transparency and co-operation and, hence, on monitoring and pervasive data collection. In highly standardized contexts, it is usually easy to gather data using available technologies, while, in complex environments, only very advanced and customizable technologies, such as Computer Vision, are intelligent enough to perform such monitoring tasks well. By the term “complex environment”, we especially refer to those contexts where human activity which cannot be fully standardized prevails. In this work, we present a Machine Vision algorithm which is able to effectively deal with human interactions inside a framed area. By exploiting inter-frame analysis, image pre-processing, binarization, morphological operations, and blob detection, our solution is able to count the pieces assembled by an operator using a real-time video input. The solution is compared with a more advanced Machine Learning-based custom object detector, which is taken as reference. The proposed solution demonstrates a very good performance in terms of Sensitivity, Specificity, and Accuracy when tested on a real situation in an Italian manufacturing firm. The value of our solution, compared with the reference object detector, is that it requires no training and is therefore extremely flexible, requiring only minor changes to the working parameters to translate to other objects, making it appropriate for plant-wide implementation.
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