A Real-time Cancer Sensing Technique using Needle Insertion Forces and Patient-specific Criteria during Percutaneous Intervention

2008 ◽  
Vol 72 (1) ◽  
pp. S48
Author(s):  
K. Yan ◽  
T. Podder ◽  
J. Sherman ◽  
L. Li ◽  
J. Joseph ◽  
...  
Keyword(s):  
Real Time ◽  
Needle Insertion ◽  
Percutaneous Intervention ◽  
Patient Specific

GPU-Based Haptic Simulator for Dental Bone Drilling

2011 ◽  
Author(s):  
Fei Zheng ◽  
WenFeng Lu ◽  
Yoke San Wong ◽  
Kelvin Weng Chiong Foong
Keyword(s):  
Real Time ◽  
Graphics Processing Units ◽  
Force Feedback ◽  
Mandibular Canal ◽  
Structure Design ◽  
Patient Specific ◽  
Force Model ◽  
Bone Drilling ◽  
The Real ◽  
Drill Bits

Dental bone drilling is an inexact and often a blind art. Dentist risks damaging the invisible tooth roots, nerves and critical dental structures like mandibular canal and maxillary sinus. This paper presents a haptics-based jawbone drilling simulator for novice surgeons. Through the real-time training of tactile sensations based on patient-specific data, improved outcomes and faster procedures can be provided. Previously developed drilling simulators usually adopt penalty-based contact force models and often consider only spherical-shaped drill bits for simplicity and computational efficiency. In contrast, our simulator is equipped with a more precise force model, adapted from the Voxmap-PointShell (VPS) method to capture the essential features of the drilling procedure. In addition, the proposed force model can accommodate various shapes of drill bits. To achieve better anatomical accuracy, our oral model has been reconstructed from Cone Beam CT, using voxel-based method. To enhance the real-time response, the parallel computing power of Graphics Processing Units is exploited through extra efforts for data structure design, algorithms parallelization, and graphic memory utilization. Preliminary results show that the developed system can produce appropriate force feedback at different tissue layers.

Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore

2021 ◽  
Vol 12 (02) ◽  
pp. 372-382
Author(s):  
Christine Xia Wu ◽  
Ernest Suresh ◽  
Francis Wei Loong Phng ◽  
Kai Pik Tai ◽  
Janthorn Pakdeethai ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Real Time ◽  
Risk Score ◽  
Patient Specific ◽  
Learning Models ◽  
Medicine Department ◽  
High Risk Patients ◽  
Risk Patients ◽  
Machine Learning Models

Abstract Objective To develop a risk score for the real-time prediction of readmissions for patients using patient specific information captured in electronic medical records (EMR) in Singapore to enable the prospective identification of high-risk patients for enrolment in timely interventions. Methods Machine-learning models were built to estimate the probability of a patient being readmitted within 30 days of discharge. EMR of 25,472 patients discharged from the medicine department at Ng Teng Fong General Hospital between January 2016 and December 2016 were extracted retrospectively for training and internal validation of the models. We developed and implemented a real-time 30-day readmission risk score generation in the EMR system, which enabled the flagging of high-risk patients to care providers in the hospital. Based on the daily high-risk patient list, the various interfaces and flow sheets in the EMR were configured according to the information needs of the various stakeholders such as the inpatient medical, nursing, case management, emergency department, and postdischarge care teams. Results Overall, the machine-learning models achieved good performance with area under the receiver operating characteristic ranging from 0.77 to 0.81. The models were used to proactively identify and attend to patients who are at risk of readmission before an actual readmission occurs. This approach successfully reduced the 30-day readmission rate for patients admitted to the medicine department from 11.7% in 2017 to 10.1% in 2019 (p < 0.01) after risk adjustment. Conclusion Machine-learning models can be deployed in the EMR system to provide real-time forecasts for a more comprehensive outlook in the aspects of decision-making and care provision.

An Encounter with Lattice Boltzmann for Biomedical Applications: Interactive Simulation to Support Clinical and Design Decisions

2021 ◽  
Author(s):  
Simone Ferrari ◽  
Simone Ambrogio ◽  
Andrew J Narracott ◽  
Adrian Walker ◽  
Paul D Morris ◽  
...  
Keyword(s):  
Real Time ◽  
Vortex Shedding ◽  
Lattice Boltzmann ◽  
Personalised Medicine ◽  
Free Field ◽  
Sudden Expansion ◽  
Patient Specific ◽  
Interactive Simulation ◽  
Straight Pipe ◽  
Karman Vortex

Abstract Medical device design for personalised medicine requires sophisticated tools for optimisation of biomechanical and biofluidic devices. This paper investigates a new real-time tool for simulating structural and fluid scenarios - ANSYS Discovery Live - and we evaluate its capability in the fluid domain through benchmark flows that all involve steady state flow at the inlet and zero pressure at the outlet. Three scenarios are reported: i. Laminar flow in a straight pipe, ii. vortex shedding from the Karman Vortex, and iii. nozzle flows as characterised by an FDA benchmark geometry. The solver uses a Lattice Boltzmann method requiring a high performance GPU (nVidiaGTX1080, 8GB RAM). Results in each case were compared with the literature and demonstrated credible solutions, all delivered in near real-time: i. The straight pipe delivered parabolic flow after an appropriate entrance length (plug flow inlet conditions), ii. the Karman Vortex demonstrated appropriate vortex shedding as a function of Reynolds number, characterised by Strouhal number in both the free field and within a pipe, and ii the FDA benchmark geometry generated results consistent with the literature in terms of variation of velocity along the centreline and in the radial direction, although deviation from experimental validation was evident in the sudden expansion section of the geometry. This behaviour is similar to previous reported results from Navier-Stokes solvers. A cardiovascular stenosis example is also considered, to provide a more direct biomedical context. The current software framework imposes constraints on inlet/outlet boundary conditions, and only supports limited control of solver discretization without providing full field vector flow data outputs. Nonetheless, numerous benefits result from the interactive interface and almost-real-time solution, providing a tool that may help to accelerate the arrival of improved patient-specific medical devices.

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