Development of a Precision Nutrition Expert System Combining AI Dietary Image Recognition and Big Data Analysis: A Case Study on Kidney Disease

Condition(s) studied

Investigational drug(s) / intervention(s)

CKDcare: Participants will use the CKDCare system, a dietitian-led LINE chatbot integrating dietary self-monitoring, personalized nutrition feedback, and nutrition education for CKD self-management.

Study summary

The goal of this interventional study is to evaluate a smartphone-based precision nutrition system for patients with chronic kidney disease (CKD).

The main questions it aims to answer are:

1. Do patients with CKD find the CKDCare system easy to use?
2. Does using the system change patients' dietary intake, including the proportion of ultra-processed foods they consume?

The system, called CKDCare, runs inside the LINE messaging app. It uses artificial intelligence to recognize food from photos, a chatbot to answer everyday diet questions, and a back-end dashboard that lets dietitians review each person's dietary records.

Participants will:

1. Use the CKDCare system in the LINE app for three months
2. Take photos of their meals and record what they eat
3. Complete a questionnaire about how easy the system is to use

Have their routine clinic blood test results and nutritional assessment reviewed as part of their usual care.

Some participants will also take part in a short interview about their experience. Dietitians and physicians who care for CKD patients will use the back-end management system and give feedback on it.

Eligibility

Primary outcome measure(s)

• System Usability Scale (SUS) score — At 3 months (end of the system trial period)
  Usability of the CKDCare system measured by the 10-item System Usability Scale. Total score ranges from 0 to 100, with higher scores indicating better perceived usability.

Trial sites (1)

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