scholarly journals Dose reconstruction for real-time patient-specific dose estimation in CT

2015 ◽  
Vol 42 (5) ◽  
pp. 2740-2751 ◽  
Author(s):  
Bruno De Man ◽  
Mingye Wu ◽  
Paul FitzGerald ◽  
Mannudeep Kalra ◽  
Zhye Yin
2012 ◽  
Vol 39 (12) ◽  
pp. 7194-7204 ◽  
Author(s):  
Neelam Tyagi ◽  
Kai Yang ◽  
David Gersten ◽  
Di Yan

2019 ◽  
Vol 46 (11) ◽  
pp. 4738-4748 ◽  
Author(s):  
Simon Skouboe ◽  
Per Rugaard Poulsen ◽  
Casper Gammelmark Muurholm ◽  
Esben Worm ◽  
Rune Hansen ◽  
...  

Author(s):  
Fei Zheng ◽  
WenFeng Lu ◽  
Yoke San Wong ◽  
Kelvin Weng Chiong Foong

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.


2021 ◽  
Vol 12 (02) ◽  
pp. 372-382
Author(s):  
Christine Xia Wu ◽  
Ernest Suresh ◽  
Francis Wei Loong Phng ◽  
Kai Pik Tai ◽  
Janthorn Pakdeethai ◽  
...  

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.


Sign in / Sign up

Export Citation Format

Share Document