research-article

A cost -effective photonics-based device for early prediction, monitoring and management of diabetic foot ulcers

Authors:

Anastasios Doulamis,

Nikolaos Doulamis,

Aikaterini AngeliAuthors Info & Claims

PETRA '20: Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments

Article No.: 63, Pages 1 - 8

https://doi.org/10.1145/3389189.3397994

Published: 30 June 2020 Publication History

PETRA '20: Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments

A cost -effective photonics-based device for early prediction, monitoring and management of diabetic foot ulcers

Pages 1 - 8

Abstract
Supplementary Material
References

Abstract

Early prediction and management of Diabetic Foot Ulcers (DFUs) is an important health factor of Europe. Recent clinical trials have concluded that NIR sensing captures oxy(deoxy)haemoglobin (HbO2, Hb) and peripheral/ tissue oxygen saturations (StO2, SpO2), thermal Infrared-IR detects hyperthermia, among Regions of Interest (ROIs) and Mid-IR contains rich information about the proteomics, lipidomics and metabolomics (e.g., glucose). Current medical approaches are i) invasive (e.g., skin lesion biopsy), ii) requires consumables, and iii) being operated by certified physicians. Our research aims at developing a non-invasive, reliable and cost-effective photonics-driven device for DFU monitoring and management which can be applied for wide use. Hyper-spectral image data are exploited for this purpose. Cost-effectiveness is achieved by introducing i) targeted photonics technologies for DFU, ii) implementing advanced signal processing/learning algorithms to increase the discrimination accuracy while maintaining hardware cost-benefit, (iii) developing a user-friendly framework operated by non-certified physicians, and even by patients, and (iv) minimizing operational cost with our non-invasive device.

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References

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Cited By

Sengottaiyan NEswaran MSungheetha ARajesh Sharma RPravin APradeep Ghantasala G(2024)Detection and Classification of Diabetic Foot Ulcers via Cloud-Based Deep Learning Approach2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON)10.1109/DELCON64804.2024.10867128(1-6)Online publication date: 21-Nov-2024
https://doi.org/10.1109/DELCON64804.2024.10867128
Sykiotis STzortzis IAngeli ADoulamis NKalogeras DMakedon F(2022)A deep-learning based diagnostic framework for Breast CancerProceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3529190.3534769(641-645)Online publication date: 29-Jun-2022
https://dl.acm.org/doi/10.1145/3529190.3534769
Cassidy BReeves NPappachan JAhmad NHaycocks SGillespie DYap M(2022)A Cloud-Based Deep Learning Framework for Remote Detection of Diabetic Foot UlcersIEEE Pervasive Computing10.1109/MPRV.2021.313568621:2(78-86)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1109/MPRV.2021.3135686

Index Terms

A cost -effective photonics-based device for early prediction, monitoring and management of diabetic foot ulcers
1. Applied computing
  1. Life and medical sciences
    1. Computational biology
      1. Imaging
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Biometrics

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PETRA '20: Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments

June 2020

574 pages

ISBN:9781450377737

DOI:10.1145/3389189

Conference Chair:
Fillia Makedon
University of Texas at Arlington

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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NSF: National Science Foundation
CSE@UTA: Department of Computer Science and Engineering, The University of Texas at Arlington
NCRS: Demokritos National Center for Scientific Research

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 June 2020

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PETRA '20

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PETRA '20: The 13th PErvasive Technologies Related to Assistive Environments Conference

June 30 - July 3, 2020

Corfu, Greece

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Cited By

Sengottaiyan NEswaran MSungheetha ARajesh Sharma RPravin APradeep Ghantasala G(2024)Detection and Classification of Diabetic Foot Ulcers via Cloud-Based Deep Learning Approach2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON)10.1109/DELCON64804.2024.10867128(1-6)Online publication date: 21-Nov-2024
https://doi.org/10.1109/DELCON64804.2024.10867128
Sykiotis STzortzis IAngeli ADoulamis NKalogeras DMakedon F(2022)A deep-learning based diagnostic framework for Breast CancerProceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3529190.3534769(641-645)Online publication date: 29-Jun-2022
https://dl.acm.org/doi/10.1145/3529190.3534769
Cassidy BReeves NPappachan JAhmad NHaycocks SGillespie DYap M(2022)A Cloud-Based Deep Learning Framework for Remote Detection of Diabetic Foot UlcersIEEE Pervasive Computing10.1109/MPRV.2021.313568621:2(78-86)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1109/MPRV.2021.3135686

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