Comparative Study of Generative Models for Early Detection of Failures in Medical Devices
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866913826122760192 |
|---|---|
| author | Sadanandan, Binesh Nobar, Bahareh Arghavani Behzadan, Vahid |
| author_facet | Sadanandan, Binesh Nobar, Bahareh Arghavani Behzadan, Vahid |
| contents | The medical device industry has significantly advanced by integrating sophisticated electronics like microchips and field-programmable gate arrays (FPGAs) to enhance the safety and usability of life-saving devices. These complex electro-mechanical systems, however, introduce challenging failure modes that are not easily detectable with conventional methods. Effective fault detection and mitigation become vital as reliance on such electronics grows. This paper explores three generative machine learning-based approaches for fault detection in medical devices, leveraging sensor data from surgical staplers,a class 2 medical device. Historically considered low-risk, these devices have recently been linked to an increasing number of injuries and fatalities. The study evaluates the performance and data requirements of these machine-learning approaches, highlighting their potential to enhance device safety. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04845 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparative Study of Generative Models for Early Detection of Failures in Medical Devices Sadanandan, Binesh Nobar, Bahareh Arghavani Behzadan, Vahid Signal Processing Machine Learning The medical device industry has significantly advanced by integrating sophisticated electronics like microchips and field-programmable gate arrays (FPGAs) to enhance the safety and usability of life-saving devices. These complex electro-mechanical systems, however, introduce challenging failure modes that are not easily detectable with conventional methods. Effective fault detection and mitigation become vital as reliance on such electronics grows. This paper explores three generative machine learning-based approaches for fault detection in medical devices, leveraging sensor data from surgical staplers,a class 2 medical device. Historically considered low-risk, these devices have recently been linked to an increasing number of injuries and fatalities. The study evaluates the performance and data requirements of these machine-learning approaches, highlighting their potential to enhance device safety. |
| title | Comparative Study of Generative Models for Early Detection of Failures in Medical Devices |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2505.04845 |