A Review for Machine Learning Applications in Characterizing Biomaterials and Biological Materials Properties

Characterizing specific mechanical properties for biological materials and biomaterials remains an exciting topic within several research fields. The functionality of biological materials and biomaterials relies on their mechanical properties, such as elastic and shear modulus. While several sophisticated experimental techniques can perform in vivo and in vitro to characterize the material properties, the measurement exhibits a broad uncertainty due to the limitations in diagnostics and experimental randomness. Alternatively, Machine learning approaches evolve as an efficient and striking tool to process a massive amount of complex data sets simultaneously and discover the hidden correlation between the materials structure and dynamic responses. This work briefly reviews the advanced applications of machine learning algorithms in studies of the dynamic behavior of biological materials and the development of biomaterials. It is evident that machine learning approaches can significantly impact the clinical development in biomedical engineering and healthcare.


Introduction
conditions is required. The non-simultaneously measurement of body fluid will also introduce more system uncertainty toward material characterization. Moreover, the rapid development of biomaterials requires the realization of mechanical properties and the understanding of their biocompatibility and their performance within biological systems. For instance, the toxicity of metallic biomaterial, chemical and thermal properties of ceramic biomaterials is within this field's scope [13]. All these requirements bring extra challenges to current experimental measurement.
Alternatively, Machine learning algorithms can overcome those limitations within current experimental techniques. They are more cost-friendly and can achieve a fast analysis of a large amount of data. The data source can either be obtained by experimental measurement or collected from the online database or previous reports. ML has progressively fascinated many researchers over the past two decades, from the application in computer science at the very beginning to broad disciplines such as engineering, biological and medical researches. There are broadly two major categories depending on learning style: supervised and unsupervised learning; while supervised learning requires user-defined labeling for input data, unsupervised learning demands the computer to discover hidden patterns from un-labeled data [14,15]. An  [16][17][18][19]. This paper briefly reviews the current state-of-the-art ML algorithms for the applications in biological materials and biomaterials, including multi-physics and multi-scales, natural or synthetic materials.

Machine Learning Application for Biological Materials
Biological materials typically refer to natural materials that are and improve the awareness of tissue damage mechanisms.
At the organ level, CNN using radio frequency data is adopted

Machine Learning Application for Bio-materials
Biomaterials play a crucial role in medical applications, such as drug delivery, tissue engineering and medical diagnostics. for the surface properties of biodegradable polymers [53]. Another interesting work is utilizing self-organizing maps (SOM) to classify polymer film types since the biomaterial surface properties control the biomaterial interaction with biological systems [54].
For metallic biomaterials, an attractive application of ML in recent years is in medical implant using the high-entropy alloys (HEAs) [55]; several machine learning methods are employed to classify phase status of HEAs and characterize the materials to targeted properties [56][57][58][59]. Besides, machine learning algorithms are emerging as a promising tool for evaluating the cytotoxicity of biomaterial nanoparticles. A unified quantitative structure toxicity relationships (QSTR) perturbation model based on ANN has been proposed to assess general cytotoxicity of the nanoparticles [60]; The Decision tree has been utilized to classify cytotoxicity based on cell viability [61]; Similar work using supervised learning method can also be found in [62,63]. Although those machine learning applications are not a direct characterization of metallic biomaterial properties, they explore the feasibility of the ML method to assess the biocompatibility of biomaterials. Another heavily used material for the implant is ceramics due to their light weight and favorable mechanical properties. Nowadays, researchers focus more on ceramic composites whose components are complex such that the traditional experimental test cannot fully depict their mechanical properties. Instead, machine learning methods can facilitate the development of novel ceramic materials and predict their specific material properties. For instance, linear/nonlinear regression machine learning algorithms and CNN are employed to design bioceramics and bioglass [64,65]. Upon reviewing current progress in the machine learning applications, it is evident that machine learning approaches have a broad range of applications in many aspects of biomaterial development, properties characterization, and material performance assessment.

Summary and Future Expectation
A better understanding of biological materials can also facilitate the development of biomaterials. Meanwhile, ML can assist in the discovery of novel biomaterials. However, the fidelity of a machine learning algorithm is highly dependent on the model architecture, model hyperparameters tuning and input data preparation. The researchers must also carefully validate the integrity of the input parameter metrics to avoid unnecessary system uncertainties.
Moreover, machine learning relies on conventional experimental and computational methods for input data collection because it is difficult to establish a comprehensive database for a wide variety of biological materials and biomaterials. However, significant progress has been made in recent years [13]. We expect broader machine learning applications in bio-related material science with the rapid evolution of more sophisticated machine learning networks and more data source availability.