Thermal and physical damage in skull base drilling using gas cooling modes: FEM simulation and experimental evaluation

Gholampour, Seifollah; Hassanalideh, Hossein Haghighi; Gholampour, Mansore; Frim, David M.

doi:10.1016/j.cmpb.2021.106463

Cited by 10 publications

(6 citation statements)

References 39 publications

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“…By vigilantly tracking these financial indicators, stakeholders can better prepare to address fiscal vulnerabilities proactively, thereby reducing the risk of similar financial challenges in the future. In addition to managing the aforementioned financial ratios based on our findings, recent advancements in medical technology also help address some challenges in the financial aspects of healthcare industries [62][63][64][65][66][67][68][69][70][71]. Therefore, technological advancements can be beneficial beyond just controlling our recognized financial ratios.…”

Section: Limitations and Future Directionsmentioning

confidence: 79%

Risk Analysis of Bankruptcy in the U.S. Healthcare Industries Based on Financial Ratios: A Machine Learning Analysis

Gholampoor,

Asadi

2024

JTAER

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The prediction of bankruptcy risk poses a formidable challenge in the fields of economics and finance, particularly within the healthcare industry, where it carries significant economic implications. The burgeoning field of healthcare electronic commerce, continuously evolving through technological advancements and changing regulations, introduces additional layers of complexity. We collected financial data from 1265 U.S. healthcare industries to predict bankruptcy based on 40 financial ratios using multi-class classification machine learning models across various industry subsectors and market capitalizations. The exceptionally high post-tuning accuracy rates, exceeding 90%, along with high-performance metrics solidified the robustness and exceptional predictive capability of the gradient boosting model in bankruptcy prediction. The results also demonstrate the power and sensitivity of financial ratios in predicting bankruptcy based on financial ratios. The Altman models highlight the return on investment (ROI) as the most important parameter for predicting bankruptcy risk in healthcare industries. The Ohlson model identifies return on assets (ROA) as an important ratio specifically for predicting bankruptcy risk within industry subsectors. Furthermore, it underscores the significance of both ROA and the enterprise value to earnings before interest and taxes (EV/EBIT) ratios as important parameters for predicting bankruptcy based on market capitalization. Recognizing these ratios enables proactive decision making that enhances resilience. Our findings contribute to informed risk management strategies, allowing for better management of healthcare industries in crises like those experienced in 2022 and even on a global scale.

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Section: Limitations and Future Directionsmentioning

confidence: 79%

Risk Analysis of Bankruptcy in the U.S. Healthcare Industries Based on Financial Ratios: A Machine Learning Analysis

Gholampoor,

Asadi

2024

JTAER

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“…It can be seen from Table 9 that there are mainly two types of research contents; one is about drill bit parameters, and the other is about with or without coolant. For the former case, in addition to the drilling speed and feed rate, literature [9,10,14] studied one parameter of the drill bit, i.e., the point angle, and literature [28] studied two parameters (point angle and helix angle); and for the latter, literature [40][41][42][43] focused on the effect of the coolant on drilling temperature, thermal damage or drilling quality. As for this work, there are three novelties: (1) more parameters (including point angle, helix angle and edge radius) of the drill bit edge shape are investigated on the premise of the predetermined rotation speed and feed rate.…”

Section: Discussionmentioning

confidence: 99%

The Influence of Bit Edge Shape Parameters on Bone Drilling Force Based on Finite Element Analysis

Huang

et al. 2022

Applied Sciences

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Bone drilling is a common surgery procedure. The drill bit shape directly affects the drilling force. Excessive drilling force may cause bone damage. In this work, on the premise of analyzing and comparing several finite element method (FEM) simulation results for drill bit of 5 mm in diameter commonly used in medical practice, a combination of drilling speed and feed rates which can minimize the drilling force for drilling parameters is determined. Then, the effects of the drill bit shape parameters including helix angle, point angle and edge radius on the drilling force are simulated by using the obtained drilling parameters, and after validation the FEM analysis results show that their variation trend is the same as the experimental one. Then, the optimum bit structure parameters are obtained through the following research: (1) the prediction model of the relationship between drill edge parameters and drilling force is established based on the result of FEM of the drilling process; (2) A particle swarm optimization algorithm is used to obtain the optimal matching parameters of the bit structure; (3) The priority order of the influence of the parameters of the bit on the drilling force is analyzed. The results show that the order of the influence is: the edge radius is the largest, the point angle is the second, and the helix angle is the smallest. The optimum combination of bit structure is that point angle, helix angle and edge radius are 95°, 35°, and 0.02 mm, respectively.

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“…Multiple groups of experiments were carried out, where unreasonable data was removed and the average value was taken. Taking the test parameters into the prediction model Equation (7), and the bone drilling temperature and model prediction temperature under each cutting parameter were recorded in Table 7. The predicted value is the temperature value obtained from the skull simulation model, and the experimental value is the temperature value obtained from the drilling process of the bovine femur.…”

Section: Case Study Of Fresh Bonementioning

confidence: 99%

“…When the surface increases the speed or feed rate, the drilling temperature increases and the coolant can effectively reduce the temperature. 7 Xu et al 8 studied the high-speed bone drilling process in dry and physiological states, and found that the drilling conditions had the greatest influence on the drilling temperature. Albert, Bruce et al 9 established a thermal injury model of bone grinding, and discussed the energy accumulation during bone grinding.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the influence of surgical robot drilling parameters on the temperature of skull drilling based on Box–Behnken design

2023

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It is easy to cause thermal damage to the bone tissue when the surgical robot performs skull drilling to remove bone flaps, due to the large diameter of the drill bit, the large heat-generating area, and the long drilling time. Therefore, in order to reduce the thermal damage during the robot-assisted skull drilling process, the relationship between the drilling parameters and the drilling temperature during the skull drilling was studied in this paper. Firstly, a dynamic numerical simulation model of the skull drilling process was established by ABAQUS, and a temperature simulation plan for skull drilling was designed based on the Box–Behnken method. Then according to the simulation results, a quadratic regression model of drill diameter, feed rate, drill speed, and drilling temperature was established by using the multiple regression method. By analyzing the regression model, the influence of drilling parameters on the drilling temperature was clarified. Finally, the bone drilling experiment was carried out, and the error percentage was lower than 10.5% through the experiment to verify the reliability of the conclusion, and a safety strategy was proposed to ensure the safety of the surgical drilling process based on this experiment.

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Thermal and physical damage in skull base drilling using gas cooling modes: FEM simulation and experimental evaluation

Cited by 10 publications

References 39 publications

Risk Analysis of Bankruptcy in the U.S. Healthcare Industries Based on Financial Ratios: A Machine Learning Analysis

Risk Analysis of Bankruptcy in the U.S. Healthcare Industries Based on Financial Ratios: A Machine Learning Analysis

The Influence of Bit Edge Shape Parameters on Bone Drilling Force Based on Finite Element Analysis

Analysis of the influence of surgical robot drilling parameters on the temperature of skull drilling based on Box–Behnken design

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