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The Use of Artificial Intelligence in Pediatric Kidney Stone Disease

Yıl 2025, Cilt: 3 Sayı: 1, 1 - 2, 01.05.2025

Öz

This letter highlights the role of AI in enhancing diagnosis and treatment in pediatric kidney stone disease. AI, especially through machine learning algorithms such as convolutional neural networks, performs highly accurately in detecting kidney stones through medical imaging-a modality that can further improve diagnostic precision and speed. AI also enables personalized treatment by analyzing a wide range of genetic, metabolic, and clinical information to tailor therapies and predict recurrence risk. With AI-enabled devices, real-time monitoring of patients can be ensured, thus helping patients maintain hydration, physical activity, and symptoms that improve their treatment adherence. Moreover, AI-powered education can engage patients through 24/7 support. In research, AI enables the discovery of novel risk factors and treatment targets. Therefore, large opportunities exist to embed AI into pediatric kidney stone management and create value in care and outcomes, for which further research and investment is necessary.

Kaynakça

  • 1. Panthier F, Melchionna A, Crawford-Smith R, et al. Can artificial intelligence accurately detect urinary stones? A systematic review. J Endourol. 2024; 38(8): 725-40.
  • 2. Yuan Q, Zhang H, Deng T, et al. Role of artificial intelligence in kidney disease. Int J Med Sci. 2020; 17(7):970-84.
  • 3. Kothamali PR, Srinivas N, Mandaloju N, Kumar Karne V. Smart healthcare: Enhancing remote patient monitoring with AI and Iot. Rev Intel Artif Med. 2023;14(1): 113-46.
  • 4. Mlakar I, Lin S, Aleksandraviča I, et al. Patients-centered SurvivorShIp care plan after cancer treatments based on big data and artificial intelligence technologies (PERSIST): A multicenter study protocol to evaluate efficacy of digital tools supporting cancer survivors. BMC Med Inform Decis Mak. 2021; 21:1-14.
  • 5. Sabuncu Z. Artificial intelligence model to assist and evaluate the kidney stone on computed tomography image. Near East University, Thesis Submitted to the Graduate School of Applied Sciences;2021.

Yıl 2025, Cilt: 3 Sayı: 1, 1 - 2, 01.05.2025

Öz

Kaynakça

  • 1. Panthier F, Melchionna A, Crawford-Smith R, et al. Can artificial intelligence accurately detect urinary stones? A systematic review. J Endourol. 2024; 38(8): 725-40.
  • 2. Yuan Q, Zhang H, Deng T, et al. Role of artificial intelligence in kidney disease. Int J Med Sci. 2020; 17(7):970-84.
  • 3. Kothamali PR, Srinivas N, Mandaloju N, Kumar Karne V. Smart healthcare: Enhancing remote patient monitoring with AI and Iot. Rev Intel Artif Med. 2023;14(1): 113-46.
  • 4. Mlakar I, Lin S, Aleksandraviča I, et al. Patients-centered SurvivorShIp care plan after cancer treatments based on big data and artificial intelligence technologies (PERSIST): A multicenter study protocol to evaluate efficacy of digital tools supporting cancer survivors. BMC Med Inform Decis Mak. 2021; 21:1-14.
  • 5. Sabuncu Z. Artificial intelligence model to assist and evaluate the kidney stone on computed tomography image. Near East University, Thesis Submitted to the Graduate School of Applied Sciences;2021.
Toplam 5 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Çocuk Nefrolojisi
Bölüm Editöre Mektup
Yazarlar

Hülya Gözde Önal

Gönderilme Tarihi 31 Ekim 2024
Kabul Tarihi 10 Aralık 2024
Yayımlanma Tarihi 1 Mayıs 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 3 Sayı: 1

Kaynak Göster

Vancouver Önal HG. The Use of Artificial Intelligence in Pediatric Kidney Stone Disease. SMJ. 2025;3(1):1-2.