Identification and ranking biomaterials for bone scaffolds using machine learning and PROMETHEE

Abstract

Purpose Bones have a complex hierarchical structure that supports their diverse chemical, biological, and mechanical functions. High rates of bone susceptibility to fractures and injury have attracted extensive research interest to find alternate biomaterials for bone scaffolds. Natural bone healing is only successful if the defect is very small and when a defect exceeds 1 cm 3 then bone grafting is required. Large bone defects or injuries are very serious problems in orthopedics as they bring great harm to health and normal function of daily life routine. A scaffold should have good strength to maintain its own structure after implantation in a load bearing environment and without being stiff that shields surrounding bone from the load. Therefore, mechanical properties of bone scaffolds should match those of the host tissue and should be part of the natural environment of the body without any harm or further damage. Methods In this paper, we present two main contributions. First, we investigate the use of machine learning models in identifying biomaterials that are suitable for bone scaffolds. Second, we rank the best materials for biomedical scaffold applications using the multi-criteria decision analysis methods, the Preference Ranking Organization METhod for the Enrichment of Evaluations (PROMETHEE). Machine learning models investigated are AdaBoost, artificial neural network (ANN), Naïve Bayes (NB), Decision tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). Mechanical properties such as comprehensive strength, tensile strength, and Young’s modulus with the cortical bone are used as the standard reference for classification. Results The results show that the ANN outperforms the other machine learning models in identifying the biomaterials suitable for bone tissue engineering, while the ranking results using PROMETHEE show that Brushite and Titanium alloy are the best appropriate biomaterials for the cancellous and cortical bones, respectively. Conclusion Brushite and Titanium alloy are the best biomaterials for the cancellous and cortical bones, respectively.

Measuring sustainable ergonomics: A hybrid multi‐criteria perspective on ergonomics indicators

“…The framework representing the phases of this research. (Abdullah et al, 2019;Dulmin & Mininno, 2003;Safari et al, 2012;Wang & Yang, 2007), health care (Amaral & Costa, 2014), finance (Albadvi et al, 2007;Bouri et al, 2002;Hababou & Martel, 1998;Mousavi & Lin, 2020), technology (Balkan & Akyüz, 2023), construction (Balali et al, 2014;Morkunaite et al, 2019;Polat, 2016), manufacturing (Anand & Kodali, 2008;Govindan et al, 2015;Vinodh & Girubha, 2012), mining (Abedi et al, 2012;Balusa & Singam, 2018;Bogdanovic et al, 2012), environment (Al-Rashdan et al, 1999Kapepula et al, 2007;Vego et al, 2008), material (Javaid et al, 2023;Maity & Chakraborty, 2015), and tourism (Lopes et al, 2018).…”

“…PROMETHEE has been widely used to solve MCDM problems for decades, whose application areas are very diversified such as management (Kilic et al, 2015; Xu & Ouenniche, 2012), water resources (Mutikanga et al, 2011; Simon et al, 2004), energy (Andreopoulou et al, 2018; Angilella & Pappalardo, 2021; Cavallaro, 2009; Doukas et al, 2006; Richter et al, 2023; Topcu & Ulengin, 2004), supply chain (Abdullah et al, 2019; Dulmin & Mininno, 2003; Safari et al, 2012; Wang & Yang, 2007), health care (Amaral & Costa, 2014), finance (Albadvi et al, 2007; Bouri et al, 2002; Hababou & Martel, 1998; Mousavi & Lin, 2020), technology (Balkan & Akyüz, 2023), construction (Balali et al, 2014; Morkunaite et al, 2019; Polat, 2016), manufacturing (Anand & Kodali, 2008; Govindan et al, 2015; Vinodh & Girubha, 2012), mining (Abedi et al, 2012; Balusa & Singam, 2018; Bogdanovic et al, 2012), environment (Al‐Rashdan et al, 1999; Kapepula et al, 2007; Vego et al, 2008), material (Javaid et al, 2023; Maity & Chakraborty, 2015), and tourism (Lopes et al, 2018).…”

Additive Manufacturing Approaches to Design Pore Structures for Development of Bioactive Scaffolds for Orthopedic Applications: A Critical Review

“…Nowadays artificial intelligence (AI) plays an increasingly important role in various fields, and healthcare is no exception. The first place where AI can play an important role be materials science and physical approaches to the microstructure of materials [93,94]. Through computer modeling, data analysis and machine learning, artificial intelligence can optimize the parameters of materials, which in theory should lead to improved biomechanical properties and biocompatibility (which is what we are striving for).…”

Development of Bioactive Scaffolds for Orthopedic Applications by Designing Additively Manufactured Titanium Porous Structures: A Critical Review

“…Modern computational solutions based on machine learning could significantly contribute to solving these problems. Several studies demonstrate a promising potential of artificial intelligence (AI) to optimize and predict a number of mechanical parameters such as compressive strength, tensile strength, shear, and Young's modulus [93,94]; to evaluate stress-shielding effect [95]; to design drug delivery systems [96]; to account for printing quality [97]; to mimic natural cellular and porous structures [98]. Very few studies report on the first encouraging attempts to link geometric and mechanical parameters of scaffolds with their physical and chemical properties correlated with in vitro and in vivo biocompatibility tests on cell-material interaction [99,100].…”