Register now After registration you will be able to apply for this opportunity online.
This opportunity is not published. No applications will be accepted.
Master Thesis / Internship: Automated Time Series Analysis in Urinary Tract Assessment in Spinal Cord Injury
The primary objective of this project is to develop an automated pipeline for the identification and recognition of patterns within urodynamic recordings, utilizing urodynamic recording data in conjunction with annotated patterns provided by experts. This endeavor seeks to reduce the susceptibility of interpreting urodynamic recordings to potential errors arising from human judgment and inaccuracies, thereby improving the management of urinary tract complications in patients with spinal cord injury. By implementing a systematic approach to pattern recognition in Bladder Valomue/Pressure Time Series Measurements of urodynamic data, the potential for error in decision-making can be significantly reduced.
Keywords: Spinal Cord Injury, Machine Learning, Deep Learning, Pattern Recognition, Feature Engineering, Time Series Analysis, Signal Processing
**Background**
Spinal cord injury (SCI) is a severe medical condition that affects millions of people worldwide. These patients often suffer from other health problems and comorbidities such as urinary tract complications which should be monitored so that the risk is minimized. A urodynamic test is a diagnostic tool used to evaluate the function of the bladder and urethra. The test measures flow and pressure in the urinary tract and can help determine the underlying cause of urinary dysfunction. The major complication of neurogenic lower urinary tract dysfunction is a risk to the upper tracts, so baseline urodynamic studies are performed in patients with SCI to assess symptoms and risk to the upper tracts, so that management can be planned accordingly. The interpretation of urodynamic measurements and the identification of patterns in the resulting graphs are critical components of the decision-making process regarding the administration of the test. However, this process is subject to a significant degree of error due to the involvement of human judgment. There is a pressing need for the development of an automated, systematic approach to pattern recognition in urodynamic data that would reduce the potential for error in decision-making.
**Your Tasks**
- Retrieve the urodynamic recordings data in conjunction with annotations and patterns provided by experts.
- Conduct an extensive review of the current literature on pattern recognition in time series data.
- Perform pre-processing procedures on the time-series recordings to eliminate artifacts and enhance data quality.
- Develop and implement pipelines utilizing deep learning or feature extraction techniques for accurate pattern recognition in time series data (employing supervised pattern recognition methods).
- Fine-tune the developed pipeline to ensure its user-friendly nature, enabling seamless adoption in future studies.
- Compile a comprehensive report that highlights the achievements, methodologies, and outcomes of the study.
- (Optional) Prepare a manuscript summarizing the project's findings for submission to a prominent and relevant academic journal, aiming to disseminate the research outcomes to the wider scientific community.
**Your Benefits**
- Gain unique access and first-hand experience in one of the leading institutions on long-term health management - At the Swiss Paraplegic Centre at Nottwil.
- Working with the potential of long-term applications from the results of this thesis work.
**Your Profile**
- Proven understanding of signal analysis and pattern recognition.
- Strong experience with Python (preferred)
- Structured and reliable working style
- Ability to work independently on a challenging topic.
**Background**
Spinal cord injury (SCI) is a severe medical condition that affects millions of people worldwide. These patients often suffer from other health problems and comorbidities such as urinary tract complications which should be monitored so that the risk is minimized. A urodynamic test is a diagnostic tool used to evaluate the function of the bladder and urethra. The test measures flow and pressure in the urinary tract and can help determine the underlying cause of urinary dysfunction. The major complication of neurogenic lower urinary tract dysfunction is a risk to the upper tracts, so baseline urodynamic studies are performed in patients with SCI to assess symptoms and risk to the upper tracts, so that management can be planned accordingly. The interpretation of urodynamic measurements and the identification of patterns in the resulting graphs are critical components of the decision-making process regarding the administration of the test. However, this process is subject to a significant degree of error due to the involvement of human judgment. There is a pressing need for the development of an automated, systematic approach to pattern recognition in urodynamic data that would reduce the potential for error in decision-making.
**Your Tasks**
- Retrieve the urodynamic recordings data in conjunction with annotations and patterns provided by experts. - Conduct an extensive review of the current literature on pattern recognition in time series data. - Perform pre-processing procedures on the time-series recordings to eliminate artifacts and enhance data quality. - Develop and implement pipelines utilizing deep learning or feature extraction techniques for accurate pattern recognition in time series data (employing supervised pattern recognition methods). - Fine-tune the developed pipeline to ensure its user-friendly nature, enabling seamless adoption in future studies. - Compile a comprehensive report that highlights the achievements, methodologies, and outcomes of the study. - (Optional) Prepare a manuscript summarizing the project's findings for submission to a prominent and relevant academic journal, aiming to disseminate the research outcomes to the wider scientific community.
**Your Benefits**
- Gain unique access and first-hand experience in one of the leading institutions on long-term health management - At the Swiss Paraplegic Centre at Nottwil. - Working with the potential of long-term applications from the results of this thesis work.
**Your Profile**
- Proven understanding of signal analysis and pattern recognition. - Strong experience with Python (preferred) - Structured and reliable working style - Ability to work independently on a challenging topic.
The main aim of this project is to utilize urodynamic recording data in conjunction with annotated patterns provided by experts to develop an automated pipeline for the identification and recognition of patterns within the recordings. By doing so, this endeavor seeks to reduce the susceptibility of interpreting urodynamic recordings to potential errors arising from human judgment and inaccuracies.
The main aim of this project is to utilize urodynamic recording data in conjunction with annotated patterns provided by experts to develop an automated pipeline for the identification and recognition of patterns within the recordings. By doing so, this endeavor seeks to reduce the susceptibility of interpreting urodynamic recordings to potential errors arising from human judgment and inaccuracies.
Host: Dr. Diego Paez (SCAI-Lab, ETHZ | SPZ)
Please send your CV and latest transcript to: Mehdi Ejtehadi (mehdi.ejtehadi@hest.ethz.ch)
Host: Dr. Diego Paez (SCAI-Lab, ETHZ | SPZ)
Please send your CV and latest transcript to: Mehdi Ejtehadi (mehdi.ejtehadi@hest.ethz.ch)