Novel Smart Assistance System for Arteriosclerosis Evaluation.

Arteriosclerosis is a cardiovascular disease that can cause calcification, sclerosis, stenosis, or obstruction of blood vessels and may further cause abnormal peripheral blood perfusion or other complications. In clinical settings, several approaches, such as computed tomography angiography and magnetic resonance angiography, can be used to evaluate arteriosclerosis status. However, these approaches are relatively expensive and require an experienced operator and often the injection of a contrast agent. In this article, a novel smart assistance system based on near-infrared spectroscopy was proposed that can noninvasively assess blood perfusion and thus indicate arteriosclerosis status. In this system, a wireless peripheral blood perfusion monitoring device simultaneously monitors changes in hemoglobin parameters and the cuff pressure applied by a sphygmomanometer. Several indexes extracted from changes in hemoglobin parameters and cuff pressure were defined and can be used to estimate blood perfusion status. A neural network model for arteriosclerosis evaluation was constructed using the proposed system. The relationship between the blood perfusion indexes and arteriosclerosis status was investigated, and the neural network model for arteriosclerosis evaluation was validated. Experimental results indicated that the differences in many blood perfusion indexes for different groups were significant and that the neural network model could effectively evaluate arteriosclerosis status (accuracy = 80.26%). By using a sphygmomanometer, the model can be employed for simple arteriosclerosis screening and blood pressure measurements. The model offers real-time noninvasive measurement, and the system is relatively inexpensive and easy to operate.

Smart Blood Vessel Detection System for Laparoscopic Surgery.

OBJECTIVE: Compared with traditional surgery, laparoscopic surgery offers the advantages of smaller scars and rapid recovery and has gradually become popular. However, laparoscopic surgery has the limitation of low visibility and a lack of touch sense. As such, a physician may unexpectedly damage blood vessels, causing massive bleeding. In clinical settings, Doppler ultrasound is commonly used to detect vascular locations, but this approach is affected by the measuring angle and bone shadow and has poor ability to distinguish arteries from veins. To tackle these problems, a smart blood vessel detection system for laparoscopic surgery is proposed. METHODS: Based on the principle of near-infrared spectroscopy, the proposed instrument can access hemoglobin (HbT) parameters at several depths simultaneously and recognize human tissue type by using a neural network. RESULTS: Using the differences in HbT and StO2 between different tissues, vascular and avascular locations can be recognized. Moreover, a mechanically rotatable stick enables the physician to easily operate in body cavities. Phantom and animal experiments were performed to validate the system's performance. CONCLUSION: The proposed system has high ability to distinguish vascular from avascular locations at various depths.