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Sensors and Materials, Vol. 34, No. 7 (2022) 2759–2769
MYU Tokyo
S & M 3004

Early Prediction of Pressure Injury
with Long Short-term Memory Networks
Xudong Fang,1,2 Yunfeng Wang,1,2 Ryutaro Maeda,1,2*
Akio Kitayama,3 and En Takashi3**
1School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

2State Key Laboratory for Manufacturing Systems Engineering, International Joint Laboratory

for Micro/Nano Manufacturing and Measurement Technology, Xi’an Jiaotong University, Xi’an 710056, China
3Nagano College of Nursing, Komagane, Nagano 399-4117, Japan(Received February 15, 2022; accepted April 19, 2022)

Keywords: early prediction, pressure injury, feature extraction, LSTM, neural networks

Early diagnosis of pressure injury has always been a challenging problem. Pressure injury
can spontaneously heal or develop into decubitus ulcers. Few methods are available to predictthe growth trend at the early stage of pressure injury, although this stage is a critical time forpreventing and treating pressure injury. To address this issue, artificial intelligence algorithms
were used in this work with image processing technology to predict the growth trend of earlystage pressure injury. A long short-term memory (LSTM) network, which is a specialized
recurrent neural network, was adopted to predict future events based on images collected fromhairless rats that made up the pressure injury models. The images were processed with ImageJsoftware to extract key features, then used to train the LSTM networks. Two types of LSTM
network were used to predict the development trend: single-variate and multivariate. The
analysis results demonstrated that multivariate LSTM is more effective than single-variate
LSTM and has high potential to be applied in the prediction of early-stage pressure injury.

1.

Introduction

Pressure injury, also known as decubitus ulcers (DU), pressure ulcers, or bedsores,(1) is a
type of partial skin ischemia caused by pressure. Stage 1 pressure injury usually involves nonblanchable erythema of intact skin.(2) At this stage, even experienced medical experts cannot
predict whether the pressure injury will spontaneously heal or develop into DU.(3) Once pressure
injury develops into DU, it causes extreme pain to patients and may be expensive totreat.(4,5) Diagnosis and monitoring of pressure injury require considerable time and effort fromhealthcare workers and has become a major and urgent problem. Nevertheless, pressure injury
can usually be healed if it is discovered early and the necessary nursing care isprovided.(6) Therefore, the early prediction of pressure injury is of great significance inprophylaxis and can avoid complicated therapy.(7)*Corresponding author: e-mail: maedaryutaro@hotmail.com**Corresponding author: e-mail: takashi@nagano-nurs.ac.jp

https://doi.org/10.18494/SAM3868

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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Few methods have been successfully applied to predict the early stage of pressure injury,
despite early prediction being an essential step in preventing the development of ulcers. Sato etal. found that although some clinical observations can be used to predict early pressure injury,
these indicators were overdependent on clinical experience and highly subjective.(8) Okonkwo et
al. conducted clinical studies on the early prediction of pressure injury using subepidermal
moisture.(9) However, their conclusion that a substantial quantity of subepidermal moisture
indicates a greater risk of pressure injury is questionable. In fact, moisture is lost throughevaporation before the skin breaks, the time when the risk of pressure injury is greatest. UVphotography was demonstrated by Takashi, a co-author of this work, to be a possible method of
detecting skin bleeding and predicting early pressure injury.(10) However, no quantitative method
for pressure injury prediction was provided. Xu et al. investigated a method using the change in
the amount of reflected UV light as an indicator to predict whether early-stage pressure injury
spontaneously heals or develops into DU, but the pressure injury region required continuous
monitoring for 18 h before the symptoms could be identified.(11) Other limitations of this process
are the high cost of the device and the inability to use it with some skin types.
Various sensors have been used in attempts to evaluate and predict pressure injury. Fard et al.
assessed the risk of pressure injury by monitoring interface pressure and temperature using 64
pressure sensors and 64 temperature sensors.(12) Their approach was based on the theory thattemperature is an indicator of pressure ulcer tendency according to the National Pressure Injury
Advisory Panel. Sakai et al. continuously monitored the interface pressure distribution in
intensive care patients using a thermoelastic polymer mattress with a KINOTEX sensor installed
in an attempt to prevent pressure ulcers.(13) Yip et al. presented a cost-effective monitoring
system to continuously detect the pressure between skin and a contact surface. The system
consisted of a flexible pressure-sensitive sheet and embedded stainless steel electrodes.(14) Drennan
and Southard designed a pressure monitoring system that triggered an alarm to inform patientsand caregivers when the soft tissue pressure exceeded a threshold value.(15) The above
researchers used various sensors and devices to investigate the relationship between pressure,temperature, and pressure ulcer development, aiming to obtain indirect indicators of pressure
ulcer development through the sensor output. However, such devices are expensive and rarely
utilized clinically.
Low-cost statistical approaches have also been studied as a means of predicting pressure
injury but are subject to controversy. Norton developed the first instrument to assess the risk ofpressure ulcer development on the basis of experience in clinical practice, in which the total risk
score was the sum of five risk factors.(16) Risk levels and total Norton scores are inversely
related. The Norton scale is simple and easy for us, but has received many criticisms because
risk factors considered important by other practitioners, such as poor nutrition, are not taken into
account.(17) The Waterlow scoring system was developed by introducing normal risk and special
risk sections to evaluate the risk of pressure ulcers, which can be considered a more advanced
and comprehensive system to evaluate the risk of pressure ulcers.(18) However, the Waterlow
scale is complex and its scope of application is relatively small.(19) Other risk assessment scales
used in clinical practice include the Braden scale.(20) However, the scales are mostly analogous,
and medical staff choose appropriate scales according to the situation in their departments.

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Although statistical methods are inexpensive, it is time-consuming and burdensome for the staff
to collect and analyze data, which wastes medical resources and makes it difficult to diagnosepressure injury in a timely manner.(21)To overcome the challenge of predicting early pressure injury, the use of artificial intelligencebased on deep learning or machine learning is a potential approach worth attempting. Different
from traditional neutral networks, recurrent neural networks (RNNs) allow the continuity of
information. Long short-term memory (LSTM), a special RNN introduced in 1997, can learn
long-term dependence.(22) Since its introduction, LSTM has been refined and developed and is
now applied in a wide range of fields. Owing to its advantages, in this study, LSTM and its
variants were used to predict early pressure injury. Multivariate and single-variate LSTM
networks were built, and their performances in the prediction of early pressure injury werecompared. We built a database based on pressure injury experiments on animals. Images werecollected, regions of interest in the images were labeled, and the corresponding features wereextracted. Symmetric mean absolute percentage error (SMAPE) and mean absolute error/
average value (MAE/MEAN) were used as evaluation criteria to assess the performance of these
networks. SMAPE, proposed by Makridakis, reduces the impact of zero and nonzero values onmodels.(23) Hoover used MAE/MEAN to overcome the problem of division by zero.(24) SMAPE
and MAE/MEAN of single-variate LSTM are 0.244 and 0.406, and the values of multivariate
LSTM are 0.198 and 0.393, respectively. These values illustrate the high performance andfeasibility of the multivariate LSTM network for predicting early pressure injury.

2.

Feature Database Obtained from Rat Images

The data this study were obtained from experiments at Nagano College of Nursing conductedon hairless rats. As shown in Fig. 1, the pressure injury model was established by exposing thedorsal skin of five hairless rats to pressure between two circular neodymium magnets for 3.75 h.
Four groups of experiments were performed on each hairless rat to obtain a total of 20 sequencesof pressure injury images. Ten of the 20 pressure injury models were DU models. The skin of therats in these 10 DU models was pressed by 4 mm magnets (440 mmHg attraction). The other 10

Fig. 1.

(Color online) Dorsal skin exposed to pressure between two circular neodymium magnets.

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pressure injury models were allowed to spontaneously heal. The dorsal skin of the rats in these
10 models was pressed by 2 mm magnets (110 mmHg attraction). In each experiment, images ofskin symptoms of the rats at 11 time points were recorded: 0.5, 3, 6, 12, 18, 24, 30, 36, 48, 72,
and 96 h. At each time point, one image of the skin under UV light with a wavelength of 405 nm
was taken by a camera specifically designed to capture dermatological symptoms (DZ-100,
Casio Co., Japan). Figure 2 shows a comparison of the same pressure injury area under whitelight and 405 nm UV light; the symptoms are clearer under the UV light. This is because
hemoglobin has high absorbance at 405 nm.(25) Olds et al. also demonstrated in forensic studies
that symptoms of subcutaneous bleeding, which are not visible under white light, can beobserved under UV light.(26) Each sequence of images in our experiment included 11 effective
images. A total of 220 (11 × 20) samples were obtained from the 20 experiments.
Because our aim was to predict whether pressure injury will develop into DU, the features of
the areas including the pressure injury and DU were the research objects. The performance ofimage processing was affected by noise and uncertainty since the images were acquired fromhairless rats. A reference point is critical in determining the relative features of the target area
because noise originates from the rotation, offset, or other factors at the same position. The
regions of interest include the areas of pressure injury and DU and the selected reference point
areas labeled by ImageJ software, as shown in Fig. 3. After comparing various features, weselected the features extracted from regions of interest, which are gray value information (mean,
maximum, and minimum gray value) of pressure injury and DU, and the absolute area of eachregion of interest, as the key features. In addition, owing to the rotation and offset of the images,
errors or noise may be introduced into the absolute area. Thus, the relative area was used torepresent the region of interest, as defined in Eq. (1). The relative area is equal to the absolute
area of pressure injury or DU divided by the reference point area.
Relative Area =(a)pressure injury ( DU ) area
reference point area

(1)

(b)

Fig. 2. (Color online) Comparison of pressure injury images obtained after 72 h of pressure under (a) white lightand (b) 405 nm light.

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Fig. 3.

3.

2763(Color online) Regions of interest in each image.

Framework of Deep Learning

Traditional neural networks can only deal with each input individually; the previous input
and the subsequent input are irrelevant. The task in the present study requires better processingof sequential information, namely, the previous input is related to the subsequent input. To solve
this problem, we used an RNN model.(28) Nevertheless, an RNN only has short-term memory
due to the inherent problems of vanishing and exploding gradients. LSTM is a type of RNN thatmitigates these problems by introducing an addition operation into the network through gate
control.(22) One of the merits of RNNs is that they can connect previous information to thecurrent task,(27) making them useful in this study. However, RNNs can only be used to solvethese problems for relatively short time intervals, and they lose their ability to learn previous
information over long time intervals.(29) Thus, we developed an LSTM network with 11 time
points to handle long-term dependence.
Multivariate LSTM, an improved version of LSTM, can be used to solve problems with morethan one characteristic parameter.(30) The first step is to transform the prediction task into
supervised learning, which simplifies the problem. Figure 4 summarizes the inputs ofmultivariate LSTM. The inputs of the network are the historical data denoted v(t−1)1 to v(t−1)8,
and the label is set to the area of the DU of the next time point.
The overall framework of the multivariate LSTM prediction model is shown in Fig. 5, which
includes four functional modules: a preprocessing block and input, hidden, and output layers.
The preprocessing block cleans the original data, transforming it to a form suitable forsupervised learning and normalizing values to the range [0, 1]. The data contained in the input
layer consists of the normalized pressure injury and DU gray level information as well as thepressure injury and DU area information at time (t−1) obtained by applying pressure to the skinof the hairless rats. The DU areas at time t are considered as labels. Each input is a vector withthe structure (9, 1). The hidden layer employs the LSTM cells to build a prediction network. The
sigmoid function was used as the activation function to produce the output of the network in adense layer. Finally, a postprocessing procedure in the output layer inversely transforms thenormalized output value to the original expression.

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Fig. 4.

Fig. 5.

Input structure of multivariate LSTM network.

(Color online) Framework of multivariate LSTM network.

We also built single-variate LSTM to compare the performance of the prediction task withthat of the multivariate LSTM. The input of the single-variate LSTM was the sequence data of
the DU areas. In the network, we first normalized the dataset and then built the LSTM networkwith only one variate. A dense layer was added to the last layer of the network to output theprediction value. Finally, an inverse normalization process was used to unify the output andinput to the same dimension in the output layer.

4.

Results and Discussion

We investigated the performance of the built networks. We divided the database into training
and validation sets with a ratio of 9:1. Figure 6 shows a comparison between actual values andvalues predicted in the final iteration at each time point, where the ordinate represents therelative area of DU and the abscissa represents the number of the time point sequence. Figure 6
demonstrates that the multivariate LSTM network has better performance in terms of the error
between the actual values and the values predicted with the training set.

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(a)
Fig. 6.

(b)(Color online) Prediction performance of (a) multivariate LSTM and (b) single-variate LSTM.

To evaluate the built networks, two popular evaluation metrics, SMAPE and MAE/MEAN,
were introduced in the work. The two metrics are calculated as
SMAPE =

yˆi − yi
100% N∑i =1 yN( ˆi + yi ) / 2 ,1
N× ∑ i =1 yˆi − yi
MAE
=N
,
1
N
MEAN
× ∑ i =1yi
N

(2)

(3)

where N is the size of the validation dataset and ŷ and y are the predicted and actual values of the
DU area, respectively. To investigate the effect of the processing parameters on the results,
different values of LSTM_units, different loss functions, different optimizers, and different
numbers of epochs were set in each model to compare the performance. The results are presented
in Table 1.
On the basis of the results in Table 1, 256 LSTM_units, Huber_loss, RMSprop, and 250
epochs were chosen as the parameters in the multivariate LSTM network to develop theprediction network. For these parameters, SMAPE and MAE/MEAN were 0.198 and 0.393,
respectively. The performance of the multivariate LSTM network with the validation set wassuperior to that with the training set as shown in Fig. 7. The loss value was only 0.0022,
indicating a relatively small error.
It was found that the model performs well in predicting the development trend of earlypressure injury. We used different evaluation indicators to show that the error between thepredicted value and the actual value obtained by the prediction network is very small, i.e., thepredicted value is very close to the actual value. This result has value for medical treatment
because it demonstrates that pressure injury can be predicted and evaluated using multivariate

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Table 1
Effect of processing parameters on performance of the two models.
LSTM_units
Loss
Optimizer
256
Huber_loss
RMSProp
Multivariate
128
Huber_loss
Adam
LSTM
64
MSE
Adam
256
Huber_loss
RMSProp
Single-variate
4
Huber_loss
RMSProp
LSTM
256
MSE
Adam

Epochs
250
100
250
100
100
100

Fig. 7.

SMAPE
0.198
0.230
0.227
0.244
0.258
0.263

MAE/MEAN
0.393
0.454
0.456
0.406
0.407
0.416

(Color online) Loss of multivariable LSTM network.

LSTM. Further development of this work is expected to lead to the realization of a suitableapproach for clinical use to assist medical staff in diagnosing pressure injury and predicting thedevelopment of symptoms.

5.

Conclusion

In this study, we built multivariate and single-variate LSTM networks to predict the growthtrend of early-stage pressure injury based on time domain features extracted from images ofhairless rats. SMAPE and MAE/MEAN were used as key parameters to evaluate theperformance of the models. The values of 0.198 and 0.393, respectively, and the loss value of
0.0022 for the multivariate LSTM network demonstrate its high accuracy and potential for
predicting early pressure injury.
In future work, more effective methods should be used to optimize the model. As the next
step, we plan to acquire frequency domain features from a data set containing more images.
There should be more ideal networks for this research, and we will continue to explore anddevelop such networks. It is possible for this method to predict not only the next time point but
also a much later time point, enabling us to achieve our goal of predicting the development ofpressure injury at a very early stage.

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Acknowledgments
The authors gratefully acknowledge coworkers in the Division of Basic & Clinical Medicine,
Nagano College of Nursing, for assistance in the experiments on pressure injury models and theacquisition and provision of image data from hairless rats. This work was supported in part by
the Japan Society for the Promotion of Science (JSPS) (Grant No. 18K10510) (ET, AK). It wasalso funded by the Recruitment Program of Global Experts (Grant No. WQ2017610445),
National Natural Science Foundation of China (No. 52175517), Zhejiang Lab (Grant No.
2022MG0AB03), China Postdoctoral Science Foundation (Grant No. 2017M610634), andShaanxi Postdoctoral Science Foundation (Grant No. 2017BSHEDZZ73).

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About the Authors
Xudong Fang is currently an associate professor at the School of MechanicalEngineering, Xi’an Jiaotong University (XJTU), China. He received his B.Sc.
and M.Sc. degree in mechanical engineering from XJTU and his Ph.D. degree
in materials science and engineering from Georgia Institute of Technology,
USA. Since then, he has been working at XJTU. His research areas include
MEMS sensors, micro/nano manufacturing and measurement, and flexible
electronics. He has led more than 20 research projects, published more than 40
academic papers, and owns 20 patents. He is also the associate editor ofInternational Journal of Nanomanufacturing.
Yunfeng Wang was born in Nanjing, Jiangsu Province, China in 1993. She iscurrently pursuing her M.S. degree in mechanical engineering at Xi’an
Jiaotong University, China. Her research interests include deep learning andimage processing in the medical field.

Ryutaro Maeda joined the Mechanical Engineering Laboratory, Agency ofAdvanced Industrial Science and Technology (AIST) in Tsukuba in 1980, after
receiving his master’s degree from the Graduate School of Tokyo University.
He is a founder of UMEMSME (Research Centre of Ubiquitous MEMS andMicro Engineering) in AIST and was their director from 2010 to 2014. He has
led several national projects, including one on green sensor networks and the
JST CREST program. He has also been a professor at Xi’an Jiaotong
University since 2018. His current research interests include the application of
wireless networked sensing for energy and safety management. He has
published more than 300 papers on MEMS and related fields.

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Akio Kitayama is currently the president and a professor at Nagano College
of Nursing (NCN), Japan. He received his B.Sc. degree in nursing from Chiba
University and his Ph.D. degree from The University of Tokyo, after which hejoined NCN. His research areas include advanced telecare systems for elderly
people in rural areas and innovative networks supporting the lives of elderly
people in satoyama (hilly and mountainous areas). He has led more than 20
research projects, published more than 40 academic papers, and owns fivepatents. He has also been a visiting professor of The University of British
Columbia, Canada, and a visiting professor of China Medical University,
China.
En Takashi is currently the chairman and a professor at Nagano College ofNursing (NCN), Japan. He received his MD degree from China Medical
University and his Ph.D. degree from Nippon Medical School (NMS). Then,he joined NMS as an assistant professor, after which, he joined NCN. His
research areas include the prevention, early diagnosis, and treatment ofpressure injury, especially elucidating the basic pathophysiology anddevelopment of new methods to detect the early stages of pressure injury. He
has led more than 30 research projects, published more than 60 academic
papers, and owns 10 patents in Japan and China. He has also been a visiting
scientist of the University of Cincinnati, USA and a visiting professor of China
Medical University, China.