1、AI Aided AKI Prediction Software
Machine learning algorithms provides AKI probability value of the patient after 24 hours.
Assist medical staff in the risk evaluation of AKI and provide care reference for high-risk patients.
Early diagnosis and intervention to reduce the influence of AKI and increase the possibility of renal function recovery.
2、AI Aided ARDS Detection Software
Utilizing machine learning algorithms, this software provides healthcare professionals with a probability score indicating the patient\\\'s risk of developing ARDS after 24 hours of invasive ventilator use. It also presents a ranked list of feature weights used by the predictive model.
This tool assists medical personnel in assessing the risk of ARDS, allowing them to focus on high-risk patients and offering a valuable reference for clinical decision-making.
Early diagnosis and early intervention in the underlying causes can reduce the severity of ARDS and improve the chances of lung function recovery.
3、AI Artificial Intelligence Hand Hygiene Recognition System
The software uses AI (artificial intelligence) CNN (convolutional neural network) algorithm and edge computing technology to perform deep learning on standard handwashing procedure video images recognition to precisely monitor the 6 major steps of proper handwashing.
4、AI Aided Weaning Trial Software
Using machine learning algorithms, this software helps healthcare professionals estimate the likelihood that a patient on an invasive ventilator for 48 hours to 21 days will be ready for extubation within the next 24 hours. It also displays a ranked list of feature contributions used in the predictive model.
This tool supports clinicians in determining the appropriate timing for extubation, enabling earlier preparation and focusing attention on high-risk ventilated patients, thereby serving as a clinical reference for patient care.
By applying Software as a Medical Device (SaMD) during the early stages of clinical assessment, it enhances decision-making efficiency, facilitates earlier extubation preparation, and ultimately reduces the duration of ventilator dependence.