Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients

Anemia, for which erythropoiesis-stimulating agents (ESAs) and iron supplements (ISs) are used as preventive measures, presents important difficulties for hemodialysis patients. Nevertheless, the number of physicians able to manage such medications appropriately is not keeping pace with the rapid increase of hemodialysis patients. Moreover, the high cost of ESAs imposes heavy burdens on medical insurance systems. An artificial-intelligence-supported anemia control system (AISACS) trained using administration direction data from experienced physicians has been developed by the authors. For the system, appropriate data selection and rectification techniques play important roles. Decision making related to ESAs poses a multi-class classification problem for which a two-step classification technique is introduced. Several validations have demonstrated that AISACS exhibits high performance with correct classification rates of 72%-87% and clinically appropriate classification rates of 92%-98%

Motion sensor-based haptic master controller with finger band soft actuator for surgical robots

This paper presents a motion sensor-based haptic master controller capable of static and dynamic force presentation by the newly invented Finger Band Soft Actuator (FBSA). The master controller operates a surgical assist robot using the 6 degrees of freedom position and posture data tracked by an optical motion capture. The haptic force presentation can be realized by the pneumatic pressure of FBSA, which can be highly integrated into the controller mechanism to maintain precise manipulability. The FBSA can present a pressing force of 3.7 N for a small-sized finger and 5.1 N for a large-sized finger at an internal pressure of 60 kPa, and there is a monotonically increasing and decreasing relationship between the air pressure and the pressing force. The effectiveness of haptic force presentation by the FBSA was experimentally evaluated in the master-follower operation of a surgical assist robot. Ten subjects performed the task of pulling a suture thread and controlled the manipulation to achieve the directed tension. The experimental result shows that with the four levels of force presentation scheme by the FBSA, the error between suture tension and target operating force is significantly smaller than without force presentation.</p

Monomolecular covalent honeycomb nanosheets produced by surface-mediated polycondensation between 1,3,5-triamino benzene and benzene-1,3,5-tricarbox aldehyde on Au(111).

Fabrication of a two-dimensional covalent network of honeycomb nanosheets comprising small 1,3,5-triamino benzene and benzene-1,3,5-tricarboxaldehyde aromatic building blocks was conducted on Au(111) in a pH-controlled aqueous solution. In situ scanning tunneling microscopy revealed a large defect-free and homogeneous honeycomb π-conjugated nanosheet at the Au(111)/liquid interface. An electrochemical potential dependence indicated that the nanosheets were the result of thermodynamic self-assembly based not only on the reaction equilibrium but also on the adsorption (partition) equilibrium, which was controlled by the building block surface coverage as a function of electrode potential

The Utility of Automated ASPECTS in Acute Ischemic Stroke for Intravenous Recombinant Tissue Plasminogen Activator (IV-rtPA) Therapy.

PURPOSE: This study aimed to investigate the accuracy and clinical significance of an artificial intelligence (AI)-based automated Alberta Stroke Program Early Computed Tomography (ASPECT) scoring software of head CT for the indication of intravenous recombinant tissue plasminogen activator (rt-PA) therapy. METHODS: This study included two populations of acute ischemic stroke: one comprised patients who had undergone head CT within 48 h of presentation (Population #1, n = 448), while the other included patients within 4.5 h from onset (Population #2, n = 132). The primary endpoint was the concordance rate of ASPECTS of the neurologists and AI software against the benchmark score. The secondary endpoints were to validate the accuracy of the neurologist and AI software in assessing the ability to rule out extensive infarction (ASPECTS of 0-5) in population #2. RESULTS: The reading accuracy of AI software was comparable to that of the board-certified vascular neurologists. The detection rate of cardiogenic cerebral embolism was better than that of atherothrombotic cerebral infarction. By excluding extensive infarction, AI-software showed a higher specificity and equivalent sensitivity compared to those of experts. CONCLUSIONS: The AI software for ASPECTS showed convincing agreement with expert evaluation and would be supportive in determining the indications of intravenous rt-PA therapy