A pressure ulcers assessment system for diagnosis and decision making using convolutional neural networks

Liu, Tom J.; Christian, Mesakh; Lee, Fa-Yauh; Chen, Yu‐Chun; Chang, Chung-Yi; Lai, Feipei; Tai, Hao–Chih

doi:10.1016/j.jfma.2022.04.010

Cited by 16 publications

(16 citation statements)

References 12 publications

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“…Some of them tried to present images into binary classes such as normal versus pressure injured (Wang, Jiang, et al, 2021) or erythema versus non-erythema (Liu, Christian, et al, 2022). Others divided PI images into multi-classes mainly as granulation/slough/necrosis (García-Zapirain et al, 2018;Zahia et al, 2018), as healthy/ burn/PI (Abubakar et al, 2020), or depending on the severity of damage as extensive/moderate/limited necrosis (Liu, Christian, et al, 2022). However, only one study classified PI images using CNN according to the PI staging system, which classifies images only into Stages 1, 2, 3 and 4 (Ay et al, 2022).…”

Section: Backg Rou N Dmentioning

confidence: 99%

“…With the increasing availability of large datasets, deep learning methodologies, in particular, CNN, are increasingly being selected for analysing medical images (Litjens et al, 2017). So far, several research works have applied CNN for PI visual classification using wound images as inputs (Abubakar et al, 2020; Ay et al, 2022; García‐Zapirain et al, 2018; Liu, Christian, et al, 2022; Wang, Jiang, et al, 2021; Zahia et al, 2018). Some of them tried to present images into binary classes such as normal versus pressure injured (Wang, Jiang, et al, 2021) or erythema versus non‐erythema (Liu, Christian, et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…So far, several research works have applied CNN for PI visual classification using wound images as inputs (Abubakar et al, 2020; Ay et al, 2022; García‐Zapirain et al, 2018; Liu, Christian, et al, 2022; Wang, Jiang, et al, 2021; Zahia et al, 2018). Some of them tried to present images into binary classes such as normal versus pressure injured (Wang, Jiang, et al, 2021) or erythema versus non‐erythema (Liu, Christian, et al, 2022). Others divided PI images into multi‐classes mainly as granulation/slough/necrosis (García‐Zapirain et al, 2018; Zahia et al, 2018), as healthy/burn/PI (Abubakar et al, 2020), or depending on the severity of damage as extensive/moderate/limited necrosis (Liu, Christian, et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Visual classification of pressure injury stages for nurses: A deep learning model applying modern convolutional neural networks

Seo

Kang

Eom

et al. 2023

Journal of Advanced Nursing

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Aims To develop a deep learning model for pressure injury stages classification based on real‐world photographs and compare its performance with that of clinical nurses to seek the opportunity of its application in clinical settings. Design This was a retrospective observational study using a deep learning model. Review Methods A plastic surgeon and two wound care nurses labelled a set of pressure injury images. We applied several modern Convolutional Neural Networks architectures and compared the performances with those of clinical nurses. Data Sources We retrospectively analysed the electronic medical records of hospitalized patients between January 2019 and April 2021. Results A set of 2464 pressure injury images were compiled and analysed. Using EfficientNet, in classifying pressure injury images, the macro F1‐score was calculated to be 0.8941, and the average performance of two experienced nurses was reported as 0.8781. Conclusion A deep learning model for classifying pressure injury images by stages was successfully developed, and the performance of the model was compared with that of experienced nurses. The classification model developed in this study is expected to help less‐experienced nurses or those working in under‐resourced healthcare settings determine the stages of pressure injury. Impact Our deep learning model can minimize discrepancies in nurses' assessment of classifying pressure injury stages. Follow‐up studies on improving the performance of deep learning models using modern techniques and clinical usability will lead to improved quality of care among patients with pressure injury. No Patient or Public Contribution Patients or the public were not involved in our research's design, conduct, reporting or dissemination plans because this was a retrospective study that used electronic medical records.

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Section: Backg Rou N Dmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Visual classification of pressure injury stages for nurses: A deep learning model applying modern convolutional neural networks

Seo

Kang

Eom

et al. 2023

Journal of Advanced Nursing

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“…Other studies were conducted after the occurrence of HAPI to predict the HAPI stages using Convolutional Neural Networks (CNN) [ 40 , 41 , 42 , 43 , 44 , 45 ]. PI images were used as input to develop a classification model.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, Ay et al classified the first four stages [ 40 ]. Liu et al classified the first two stages [ 42 ]. On the other hand, Matsumoto et al classified four different patterns within DTPI [ 45 ].…”

Section: Introductionmentioning

confidence: 99%

An Integrated System of Braden Scale and Random Forest Using Real-Time Diagnoses to Predict When Hospital-Acquired Pressure Injuries (Bedsores) Occur

Dweekat

Lam

McGrath

2023

IJERPH

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Background and Objectives: Bedsores/Pressure Injuries (PIs) are the second most common diagnosis in healthcare system billing records in the United States and account for 60,000 deaths annually. Hospital-Acquired Pressure Injuries (HAPIs) are one classification of PIs and indicate injuries that occurred while the patient was cared for within the hospital. Until now, all studies have predicted who will develop HAPI using classic machine algorithms, which provides incomplete information for the clinical team. Knowing who will develop HAPI does not help differentiate at which point those predicted patients will develop HAPIs; no studies have investigated when HAPI develops for predicted at-risk patients. This research aims to develop a hybrid system of Random Forest (RF) and Braden Scale to predict HAPI time by considering the changes in patients’ diagnoses from admission until HAPI occurrence. Methods: Real-time diagnoses and risk factors were collected daily for 485 patients from admission until HAPI occurrence, which resulted in 4619 records. Then for each record, HAPI time was calculated from the day of diagnosis until HAPI occurrence. Recursive Feature Elimination (RFE) selected the best factors among the 60 factors. The dataset was separated into 80% training (10-fold cross-validation) and 20% testing. Grid Search (GS) with RF (GS-RF) was adopted to predict HAPI time using collected risk factors, including Braden Scale. Then, the proposed model was compared with the seven most common algorithms used to predict HAPI; each was replicated for 50 different experiments. Results: GS-RF achieved the best Area Under the Curve (AUC) (91.20 ± 0.26) and Geometric Mean (G-mean) (91.17 ± 0.26) compared to the seven algorithms. RFE selected 43 factors. The most dominant interactable risk factors in predicting HAPI time were visiting ICU during hospitalization, Braden subscales, BMI, Stimuli Anesthesia, patient refusal to change position, and another lab diagnosis. Conclusion: Identifying when the patient is likely to develop HAPI can target early intervention when it is needed most and reduces unnecessary burden on patients and care teams when patients are at lower risk, which further individualizes the plan of care.

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Region-Based Semi-Two-Stream Convolutional Neural Networks for Pressure Ulcer Recognition

Zalluhoğlu,

Akdoğan,

Karakaya

et al. 2024

J Digit Imaging. Inform. med.

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A pressure ulcers assessment system for diagnosis and decision making using convolutional neural networks

Cited by 16 publications

References 12 publications

Visual classification of pressure injury stages for nurses: A deep learning model applying modern convolutional neural networks

Visual classification of pressure injury stages for nurses: A deep learning model applying modern convolutional neural networks

An Integrated System of Braden Scale and Random Forest Using Real-Time Diagnoses to Predict When Hospital-Acquired Pressure Injuries (Bedsores) Occur

Region-Based Semi-Two-Stream Convolutional Neural Networks for Pressure Ulcer Recognition

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