**1. Introduction**

War trauma data are the core elements of wargaming, military medical service training, and medical decision-making [1]. With the continuous development of modern warfare, the analysis and research of physical war trauma data have become more and more important. However, the amount of existing data is not sufficient to support large-scale analysis and evaluation, and the confidential nature of war trauma data makes them hard to collect and obtain from public channels. Therefore, efficient and credible data augmentation of war trauma data has become a research work with great practical significance. To the best of our knowledge, research on this topic has been limited. In the currently used method, the additional physical trauma data are still artificially generated by well-trained experts or doctors based on their professional knowledge and experience. However, this method is not only inefficient, time-consuming, and labor cost-intensive, but also inherently biased due to its dependence on personal subjective cognition, which is difficult to overcome. In addition, different experts have no unified standard for assessing injury consequences. Furthermore, the amount of artificially generated war trauma data is too small to meet the actual needs. Therefore, we developed a standardized evaluation algorithm to improve the quality of assessment of injury consequences and find an automatic, efficient, and credible approach for small-sample augmentation of war trauma data.

More than half a century since the concept of artificial intelligence (AI) was first formally proposed at the Dartmouth Conference [2], the AI technology has empowered

**Citation:** Yin, J.; Zhao, P.; Zhang, Y.; Han, Y.; Wang, S. A Data Augmentation Method for War Trauma Using the War Trauma Severity Score and Deep Neural Networks. *Electronics* **2021**, *10*, 2657. https://doi.org/10.3390/ electronics10212657

Academic Editor: Enzo Pasquale Scilingo

Received: 16 September 2021 Accepted: 26 October 2021 Published: 29 October 2021

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**Copyright:** © 2021 by the authors. 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/).

amazing developments in many fields. Meanwhile, the external environment and challenges faced by the development of AI have also undergone profound changes [3]. These changes are especially prominent in certain fields, such as big data, virtual reality, supercomputing, and mobile payment. Therefore, under the trend that the overall environment is getting closer to big data, deep learning (DL), which is based on machine learning, has become the core element of the application of AI [4] and has led to satisfactory application results in many fields, such as cloud computing [5], image identification [6], sports training [7], and AlphaGo [8]. Recently, AI technologies such as DL started to be gradually applied in the field of medical research, including in promoting disease management [9], computer-aided diagnosis [10], biomedical information processing [11], medical image recognition [12], and disease prediction [13]. Especially in disease prediction, AI has been recognized as one of the key elements of an accurate and robust prediction system [14]. For example, deep neural networks (DNNS), which are AI tools, are now used to assist physicians and for automatic diagnosis. Specific application cases include early detection of cardiovascular disease [15], cancer diagnosis [16], survival prediction [17], and injury severity assessment [18].

Compared with machine-learning methodologies and shallow neural networks, DL, which is now the core of the AI method, overcomes the research drawbacks of limited samples and low generalizability by training large-scale annotated sample data to automatically extract complex sample features and fully optimize the model parameters layer by layer. Thus, DL can carry out a more essential characterization of the data and demonstrates a superior feature-learning ability [19]. In other words, with the existing technology level, the larger the scale and the higher the quality of the annotated data are, the better the performance of the model will be. Therefore, DL can effectively solve many complex problems in the medical field [20,21]. In the prediction and diagnosis of some diseases, the accuracy and efficiency of predictive DL models have surpassed those of professional doctors and experts [22] and have thus made outstanding contributions to the development of the medical field.

#### **2. Related Work**

Currently, there are two main methods of data augmentation: oversampling and generative adversarial network (GAN). The principle of oversampling is as follows: if the samples of different classes are imbalanced, the training data can be expanded by copying the training samples of the minority class or adding noises to create new ones [23]. To solve the imbalanced dataset learning problem, in 2002, Bowyer et al. [24] created a synthetic minority oversampling technique (SMOTE), which generated synthetic minority class samples. In 2005, Han et al. [25] proposed a borderline SMOTE algorithm, which considered the minority instances near the borderline and the neighboring instances. The following year, David et al. [26] proposed a cluster SMOTE; Bai et al. [27] proposed an adaptive synthetic sampling approach (ADASYN) for imbalanced learning in 2008; Barua et al. [28] suggested a MWMOTE in 2014; Douzas et al. [29] proposed a SOMO method in 2017. Most of these methods focused on imbalanced learning by adding oversampling examples to the imbalanced datasets. However, physical war trauma data are not imbalanced but insufficient in every class. Therefore, the abovementioned oversampling techniques are not suitable for the augmentation of physical war trauma data.

A GAN is a data augmentation model based on DL, which can be used to learn the potential distribution of complex data, generate large-scale and high-quality synthetic samples, effectively solve the problem of insufficient data due to factors such as difficulty and cost of sample acquisition [30]. Thus, the GAN has become one of the most promising data augmentation approaches in recent years. A GAN is intrinsically a generation model [31] that does not depend on a priori hypotheses but on the internal confrontation between the data and the model itself to achieve unsupervised learning. To solve the inadequate problem of real data, a GAN can generate synthetic samples of the existing data with the same distribution [32]. A GAN's structure consists of two feedforward neural networks: a generator G and a discriminator D. In the learning process, G continuously generates new synthetic samples while D discriminates between the synthetic samples and the real samples as accurately as possible, then gives feedback. In this way, the GAN has created a game similar to "counterfeit currency identification" in which both sides of the game continue to improve their abilities through confrontation.

However, the samples processed by a GAN are mainly two-dimensional data such as pictures and voice signals. A GAN generates virtual images by rotating, scaling, cropping, and changing the brightness, contrast, hue, saturation and adding random noise to image data. However, the GAN is not a good choice for augmenting physical war trauma data.

In the medical field, the application of medical scoring is increasingly maturing, especially in medical treatment, early diagnosis, trauma assessment, and other aspects to the point that it now plays an important auxiliary role. For example, Gabriele Canzi et al. introduced the comprehensive facial injury (CFI) score for comprehensively evaluating severity of facial injuries [33]. Hasanka Ratnayake et al. used a laboratory-derived early warning score to predict in-hospital mortality and admission to the intensive care unit (ICU) [34]. Konlawij Trongtrakul et al. created the acute kidney injury (AKI) risk prediction score for early prediction of the condition among critically ill surgical patients [35].

The trauma score is a common type of medical score that predicts severity of an injury. It uses scientific scoring to quantitatively or semi-quantitatively assess injury severity and its consequences to the injured [36]. The scoring standard was developed by a panel of experts in the field who will continue to improve and optimize it based on feedback from the application of the trauma score as well as from related research progress. Recently, several improved injury severity score (ISS) methods have been proposed. Cristiane et al. created a novel trauma and injury severity score (TRISS) for survival prediction [37]; Yang et al. used a revised injury severity score (RISS) to evaluate the severity of injuries of patients hospitalized due to an accident [38]; Shi et al. developed a weighted injury severity score (WISS) to improve adult trauma mortality prediction [39]. For example, RISS divides the human body into six public parts: the head, the face, the chest, the abdomen, the limbs, and the body surface. Then, it squares the standard ISS for each of the most serious injuries of the three most serious body parts of the patient and puts them together. As for the second most serious injuries, only their ISS values are put together. If there are more than four injured parts, the standard injury severity score of the most serious injuries of the fourth part is added. The RISS equation is as follows:

$$RISS = (A\_1^2 + A\_2) + (B\_1^2 + B\_2) + (C\_1^2 + C\_2) + D \tag{1}$$

where *A*1, *B*1, and *C*<sup>1</sup> mean the most serious injuries of the three most serious body parts; *A*2, *B*2, and *C*<sup>2</sup> mean the second most serious injuries of the three most serious body parts; *D* means the standard injury severity score of the most serious injuries of the fourth part.

Taken together, various novel scientific scoring methods have gradually become doctors' helping hands in evaluating patients' injuries. Medical scoring belongs to the category of predictive science. Because different scoring mechanisms have different limitations, it is impossible to achieve 100% accuracy in prediction. However, with the continuous advancements in medicine and with the revision, expansion, and improvement of the scoring mechanisms by researchers in the related domains, medical scoring approaches are expected to become more scientific, practical, and in line with objective reality [40].

On the other hand, the DL technology combined with knowledge from different disciplines for interdisciplinary field research is an emerging trend. For example, Yang et al. enhanced PIR-based multiperson localization by combining DL with the domain knowledge [41], and Ding et al. combined the domain knowledge and DL for domain adaptation in machine translation [42]. Therefore, combining DL with the domain knowledge of medical experts according to the characteristics of war trauma data is key to the successful application of DL to the augmentation of war trauma data.

Based on the above research, to solve the data augmentation problems with smallsample war trauma data by studying the GAN's idea and the medical trauma scoring

method, this article proposes an approach that combines a WTSS with a DNN [43]. The WTSS–DNN integrated model simulates the generative model in thought, including sample generation and discrimination. The injuries are generated through random sampling and evaluated with WTSS, and then marked with an injury consequence label; this is the sample generation link. The assessment of the prediction accuracy of the DNN classifier is combined with the discrimination of unreasonable injuries by the expert panel; this is the discrimination link. After the accuracy and plausibility of the synthetic samples have been judged, the expert panel provides feedback, based on which, on the one hand, the characteristics of the synthetic samples are further investigated while the necessary optimization and adjustments to the WTSS algorithm are made; and on the other hand, the unreasonable synthetic samples are filtered out to improve data rationality. Eventually, the accuracy and plausibility of the augmented data are expected to stabilize and be optimized to generate credible samples.

This data augmentation approach is the first attempt to combine war trauma assessment in the medical field with DL in the AI field. The WTSS–DNN integrated model can automatically generate large-scale and credible virtual war trauma data, making it possible to carry out related data-based military research, which has great practical significance. In addition, this approach not only helps to solve the war trauma data augmentation problem, but the WTSS algorithm we have proposed also provides a practical auxiliary tool for quickly evaluating soldiers' injuries and formulating treatment strategies.
