We analyzed data from 976 Chilean participants integrating a comprehensive set of demographic, lifestyle, and health factors into our predictive model. Unlike previous research focusing mainly on diagnostic imaging or isolated risk factors,1 our study offers a holistic assessment of urolithiasis risk. Significantly, the model’s identification of hydration levels and dietary patterns provides actionable insights for patient counseling and prevention strategies.
Key aspects of our methodology included:
- Questionnaire Design and Data Collection: A collaborative effort among urologists, nephrologists, and nutritionists resulted in a detailed questionnaire capturing relevant data on demographics, nutrition, physical activity, medical and family history, and recent urine analyses.
- Inclusion and Exclusion Criteria: Participants were required to be at least 18 years old and capable of completing the survey comprehensively. We excluded individuals with severe mental or physical conditions impeding smartphone usage and those unable to provide informed consent.
- Machine Learning Model Development: A supervised ML model was developed using Python, employing statistical methods to enhance model interpretability.
Our study identified several significant predictors of urolithiasis:
- Hydration and Fluid Intake: Low water intake and dark urine color emerged as strong predictors of kidney stone formation emphasizing the protective role of adequate hydration.
- Physical Activity: Increased frequency of physical activity was associated with a reduced risk of kidney stones, highlighting the importance of regular exercise in stone prevention.
- Dietary Patterns: Higher fruit and vegetable intake, balanced dairy consumption, and specific protein sources played crucial roles in reducing kidney stone risk.
- Gender Disparities: Males were found to be over twice as likely to develop kidney stones compared to females.
The use of AI in urolithiasis management marks a significant shift towards more precise and personalized healthcare. By tailoring preventive strategies based on individual risk profiles, we can enhance patient well-being and reduce the incidence of kidney stones. Furthermore, deploying AI tools in clinical settings can streamline resource allocation and improve healthcare efficiency.
Future applications of our model include integrating these technologies into clinical practice to assess a patient’s risk of stone formation. This would allow for targeted lifestyle modifications to reduce future risks. To ensure successful clinical implementation, it is crucial to train the technology on increasingly diverse and extensive datasets to enhance predictive accuracy.
AI’s role in healthcare promises improved clinical outcomes, greater efficiency, and personalized patient care. By enabling early disease detection and facilitating the development of tailored preventive strategies, AI heralds a new era of patient-centric and data-driven medical practice. Explore our full manuscript to learn more about our pioneering approach to urological care.
Written by:
- Juan Sebastian Arroyave, BS, 3rd-Year Medical Student, Icahn School of Medicine at Mount Sinai, New York, NY
- Francisca Larenas, MD, Urology Department, University of Chile, Hospital Clínico San Borja Arriarán, Santiago, Chile
- Juan Fulla, MD, MSc, Urology Department, University of Chile, Hospital Clínico San Borja Arriarán, Santiago, Chile.
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