Cargando…
Diabetic Foot Ulcer Identification: A Review
Diabetes is a chronic condition caused by an uncontrolled blood sugar levels in the human body. Its early diagnosis may prevent severe complications such as diabetic foot ulcers (DFUs). A DFU is a critical condition that can lead to the amputation of a diabetic patient’s lower limb. The diagnosis of...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297618/ https://www.ncbi.nlm.nih.gov/pubmed/37370893 http://dx.doi.org/10.3390/diagnostics13121998 |
_version_ | 1785063924855472128 |
---|---|
author | Das, Sujit Kumar Roy, Pinki Singh, Prabhishek Diwakar, Manoj Singh, Vijendra Maurya, Ankur Kumar, Sandeep Kadry, Seifedine Kim, Jungeun |
author_facet | Das, Sujit Kumar Roy, Pinki Singh, Prabhishek Diwakar, Manoj Singh, Vijendra Maurya, Ankur Kumar, Sandeep Kadry, Seifedine Kim, Jungeun |
author_sort | Das, Sujit Kumar |
collection | PubMed |
description | Diabetes is a chronic condition caused by an uncontrolled blood sugar levels in the human body. Its early diagnosis may prevent severe complications such as diabetic foot ulcers (DFUs). A DFU is a critical condition that can lead to the amputation of a diabetic patient’s lower limb. The diagnosis of DFU is very complicated for the medical professional as it often goes through several costly and time-consuming clinical procedures. In the age of data deluge, the application of deep learning, machine learning, and computer vision techniques have provided various solutions for assisting clinicians in making more reliable and faster diagnostic decisions. Therefore, the automatic identification of DFU has recently received more attention from the research community. The wound characteristics and visual perceptions with respect to computer vision and deep learning, especially convolutional neural network (CNN) approaches, have provided potential solutions for DFU diagnosis. These approaches have the potential to be quite helpful in current medical practices. Therefore, a detailed comprehensive study of such existing approaches was required. The article aimed to provide researchers with a detailed current status of automatic DFU identification tasks. Multiple observations have been made from existing works, such as the use of traditional ML and advanced DL techniques being necessary to help clinicians make faster and more reliable diagnostic decisions. In traditional ML approaches, image features provide signification information about DFU wounds and help with accurate identification. However, advanced DL approaches have proven to be more promising than ML approaches. The CNN-based solutions proposed by various authors have dominated the problem domain. An interested researcher will successfully be able identify the overall idea in the DFU identification task, and this article will help them finalize the future research goal. |
format | Online Article Text |
id | pubmed-10297618 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102976182023-06-28 Diabetic Foot Ulcer Identification: A Review Das, Sujit Kumar Roy, Pinki Singh, Prabhishek Diwakar, Manoj Singh, Vijendra Maurya, Ankur Kumar, Sandeep Kadry, Seifedine Kim, Jungeun Diagnostics (Basel) Review Diabetes is a chronic condition caused by an uncontrolled blood sugar levels in the human body. Its early diagnosis may prevent severe complications such as diabetic foot ulcers (DFUs). A DFU is a critical condition that can lead to the amputation of a diabetic patient’s lower limb. The diagnosis of DFU is very complicated for the medical professional as it often goes through several costly and time-consuming clinical procedures. In the age of data deluge, the application of deep learning, machine learning, and computer vision techniques have provided various solutions for assisting clinicians in making more reliable and faster diagnostic decisions. Therefore, the automatic identification of DFU has recently received more attention from the research community. The wound characteristics and visual perceptions with respect to computer vision and deep learning, especially convolutional neural network (CNN) approaches, have provided potential solutions for DFU diagnosis. These approaches have the potential to be quite helpful in current medical practices. Therefore, a detailed comprehensive study of such existing approaches was required. The article aimed to provide researchers with a detailed current status of automatic DFU identification tasks. Multiple observations have been made from existing works, such as the use of traditional ML and advanced DL techniques being necessary to help clinicians make faster and more reliable diagnostic decisions. In traditional ML approaches, image features provide signification information about DFU wounds and help with accurate identification. However, advanced DL approaches have proven to be more promising than ML approaches. The CNN-based solutions proposed by various authors have dominated the problem domain. An interested researcher will successfully be able identify the overall idea in the DFU identification task, and this article will help them finalize the future research goal. MDPI 2023-06-07 /pmc/articles/PMC10297618/ /pubmed/37370893 http://dx.doi.org/10.3390/diagnostics13121998 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Das, Sujit Kumar Roy, Pinki Singh, Prabhishek Diwakar, Manoj Singh, Vijendra Maurya, Ankur Kumar, Sandeep Kadry, Seifedine Kim, Jungeun Diabetic Foot Ulcer Identification: A Review |
title | Diabetic Foot Ulcer Identification: A Review |
title_full | Diabetic Foot Ulcer Identification: A Review |
title_fullStr | Diabetic Foot Ulcer Identification: A Review |
title_full_unstemmed | Diabetic Foot Ulcer Identification: A Review |
title_short | Diabetic Foot Ulcer Identification: A Review |
title_sort | diabetic foot ulcer identification: a review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297618/ https://www.ncbi.nlm.nih.gov/pubmed/37370893 http://dx.doi.org/10.3390/diagnostics13121998 |
work_keys_str_mv | AT dassujitkumar diabeticfootulceridentificationareview AT roypinki diabeticfootulceridentificationareview AT singhprabhishek diabeticfootulceridentificationareview AT diwakarmanoj diabeticfootulceridentificationareview AT singhvijendra diabeticfootulceridentificationareview AT mauryaankur diabeticfootulceridentificationareview AT kumarsandeep diabeticfootulceridentificationareview AT kadryseifedine diabeticfootulceridentificationareview AT kimjungeun diabeticfootulceridentificationareview |