Smart Monitoring for Cancer Treatment: Feasibility Study of an IoT-Based Assessment System

Area of Science:

• Oncology
• Digital Health
• Biomedical Engineering

Background:

• Existing cancer care monitoring often overlooks comprehensive patient health, focusing on isolated parameters.
• Non-invasive monitoring technologies are crucial for improving patient outcomes in oncology.
• There's a need for integrated systems that provide a holistic view of patient well-being during treatment.

Purpose of the Study:

• To evaluate the feasibility of an Internet of Things (IoT)-based system for detecting treatment-related issues in cancer patients.
• To integrate wearable sensors, physiological data, and patient-reported outcomes for continuous monitoring.
• To explore the application of Artificial Intelligence (AI) in analyzing heterogeneous data for early complication detection.

Main Methods:

• A cohort of 26 oncology patients used a monitoring kit (smartwatch, tensiometer, weighing scale, mobile device).
• Data collected included continuous physiological signals, daily blood pressure, weekly weight, and patient-reported side effects.
• Rule-based algorithms transformed data into clinical metrics; Machine Learning models analyzed features for complication alerts.

Main Results:

• Demonstrated the feasibility of integrating IoT devices and AI for continuous oncology patient monitoring.
• Successfully transformed diverse data streams into actionable clinical metrics.
• Developed a system capable of generating alerts for potential treatment complications.

Conclusions:

• The integration of IoT and AI is feasible for patient-centered oncology monitoring.
• This approach supports intelligent decision-making for enhanced early detection and clinical management.
• The system offers a pathway to more comprehensive and proactive cancer care.