The Applications of Artificial Neural Networks in Clinical Biochemistry
The Applications of Artificial Neural Networks in Clinical Biochemistry
Ali hakimzade,1,*Golnaz Ansarihadipour,2Hadi Ansarihadipour,3
1. Payam Noor University of Tehran, East Tehran Branch. 2. Veterinary Faculty, Islamic Azad University, Karaj Branch, Karaj, Iran. 3. Department of Biochemistry and Genetics, School of Medicine, Arak University of Medical Sciences, Arak, Iran
Introduction: Artificial neural networks (ANNs) are inspired by the biological nervous system and are designed as computational models to recognize patterns and relationships in data sets. In clinical biochemistry, ANNs have been employed to analyze complex biochemical data, leading to significant advancements in diagnosis, prognosis, and treatment of diseases.
Methods: Multilayer Perceptron(MLP) and Radial Basis Function (RBF) are two main methods of ANN. Feed forward MLP has the best performance in biochemical analysis.
Results: Applications of ANNs in Clinical Biochemistry
ANNs can be widely used in diagnosing various diseases by analyzing biochemical markers. For instance, they have been used to predict the onset of diabetes by analyzing glucose levels and other related biomarkers. Similarly, ANNs have shown promise in cancer detection by identifying specific tumor markers. Predictive models which are using ANNs can forecast disease progression and patient outcomes. For example, ANNs have been used to predict acute kidney injury by analyzing patient data, including serum creatinine levels and other biochemical parameters. These models help clinicians make informed decisions about patient care and treatment strategies. The ability of ANNs to analyze large datasets allows for the development of personalized treatment plans. By examining individual patient data, including genetic information and biochemical profiles, ANNs can recommend tailored therapies that maximize efficacy and minimize adverse effects.
Benefits on ANNS
ANNs can process vast amounts of data with high precision and are leading to more accurate diagnoses and predictions. The accuracy is particularly beneficial in identifying subtle patterns that may be missed by human experts. The automation of data analysis through ANNs reduces the time required for diagnostic processes and allowing for quicker decision-making and improved patient outcomes. ANNs can handle large-scale data from diverse sources, making them suitable for widespread clinical applications. This scalability is crucial in modern healthcare settings where data volume is continually increasing.
Challenges of ANNs
The performance of ANNs is heavily dependent on the quality and quantity of data. Inconsistent or incomplete data can lead to inaccurate predictions and diagnoses. Ensuring high-quality data collection and management is essential for the effective use of ANNs. One of the significant challenges with ANNs is their “black box” nature, where the decision-making process is not easily interpretable. This lack of transparency can hinder clinical adoption, as healthcare professionals may be reluctant to rely on systems that they do not fully understand. The use of ANNs in clinical settings raises regulatory and ethical concerns, particularly regarding patient data privacy and the potential for biased algorithms. Establishing precise regulatory frameworks and ethical guidelines is necessary to address these issues.
Conclusion: Artificial neural networks have been considered as powerful tools in clinical biochemistry, offering significant advancements in disease diagnosis, predictive analysis, and personalized medicine. While challenges remain, the continued evolution of ANNs and their integration into clinical workflows hold the promise of transforming healthcare measures and improving patient outcomes.