Table 1 Comparison of numerous technologies used and their limitations on related works.
From: Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection
Study | Approach | Technology Used | Strengths | Limitations |
|---|---|---|---|---|
Makroum et al. (2022) | Wearable sensor-based health monitoring | Wearable sensors, AI techniques | Continuous monitoring, non-invasive | High energy consumption, motion artifacts |
Nagy et al. 12 | Camera-based monitoring in NICUs | Camera systems, vital sign algorithms | Non-contact, continuous monitoring | Requires precise camera setup |
Paul et al. 13 | Wireless sensor-based respiratory anomaly detection | DL, wireless sensors | Energy-efficient, non-invasive | Focused on respiratory issues only |
Mohebbi et al. 22 | CGM signals for diabetes detection | Continuous Glucose Monitoring (CGM), Deep Learning | High accuracy in diabetes prediction | Limited to diabetes detection |
Pickhardt et al. 16 | Biomarker identification via abdominal CT imaging | Abdominal CT imaging, DL | Utilizes existing health records | Requires advanced imaging techniques |
Esteva et al. 23 | Healthcare prediction and trajectory analysis | DL, Electronic Health Records | Comprehensive analysis of patient data | Potential bias in prediction models |
