Predicting Cell Viability from Titanium Surface Properties Using Machine Learning-Based Decision Tree Analysis

This paper introduces a computational tool designed to assist in classifying and predicting in vitro cellular activity using a dataset derived from the roughness, wettability, and surface morphology of titanium dioxide (TiO2) and titanium (Ti) surfaces. Numerous studies compare TiO2/Ti surface treatments to enhance osteoblast cellular activity; however, critical gaps remain in understanding how surface properties influence cellular responses. This research compiles a dataset based on peer-reviewed scientific articles published on academic platforms, focusing on surface characteristics: roughness, contact angle, presence of nanostructures such as nanotubes, and the percentage gain in cellular viability of MC3T3-E1 osteoblasts obtained from MTT assays (3-(4, 5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide), relative to control samples. Using this data, an Index of Classification of Increased Cell Activity (ICICA) was developed to categorize cellular responses into three levels: low, medium and high. Using the constructed dataset, decision tree algorithms were applied to develop a model capable of predicting cellular viability. Among the tested algorithms, the Random Forest model demonstrates superior performance regarding accuracy and Kappa coefficient. The developed model provides valuable insights to guide the design of new surface treatments regarding surface properties, such as roughness, wettability, and morphology of pure Ti, aiming to improve the cellular viability.

• Publication year

  2025

• Venue

  Materials Research

• Publication date

  Unknown publication date

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1590/1980-5373-mr-2025-0313
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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