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Preserving privacy in surgical video analysis using a deep learning classifier to identify out-of-body scenes in endoscopic videos.

Scientific reports
Surgical video analysis facilitates education and research. However, video recordings of endoscopic surgeries can contain privacy-sensitive information, especially if the endoscopic camera is moved out of the body of patients and out-of-body scenes a...

Generating synthetic personal health data using conditional generative adversarial networks combining with differential privacy.

Journal of biomedical informatics
A large amount of personal health data that is highly valuable to the scientific community is still not accessible or requires a lengthy request process due to privacy concerns and legal restrictions. As a solution, synthetic data has been studied an...

ChatGPT in Colorectal Surgery: A Promising Tool or a Passing Fad?

Annals of biomedical engineering
Colorectal surgery is a specialized branch of surgery that involves the diagnosis and treatment of conditions affecting the colon, rectum, and anus. In the recent years, the use of artificial intelligence (AI) has gained considerable interest in vari...

Ensuring privacy protection in the era of big laparoscopic video data: development and validation of an inside outside discrimination algorithm (IODA).

Surgical endoscopy
BACKGROUND: Laparoscopic videos are increasingly being used for surgical artificial intelligence (AI) and big data analysis. The purpose of this study was to ensure data privacy in video recordings of laparoscopic surgery by censoring extraabdominal ...

Commitments for Ethically Responsible Sourcing, Use, and Reuse of Patient Data in the Digital Age: Cocreation Process.

Journal of medical Internet research
BACKGROUND: Personal information, including health-related data, may be used in ways we did not intend when it was originally shared. However, the organizations that collect these data do not always have the necessary social license to use and share ...

Privacy risks of whole-slide image sharing in digital pathology.

Nature communications
Access to large volumes of so-called whole-slide images-high-resolution scans of complete pathological slides-has become a cornerstone of the development of novel artificial intelligence methods in pathology for diagnostic use, education/training of ...

Neural gradient boosting in federated learning for hemodynamic instability prediction: towards a distributed and scalable deep learning-based solution.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Federated learning (FL) is a privacy preserving approach to learning that overcome issues related to data access, privacy, and security, which represent key challenges in the healthcare sector. FL enables hospitals to collaboratively learn a shared p...

Privacy preserving for AI-based 3D human pose recovery and retargeting.

ISA transactions
As an essential research task in artificial intelligence (AI), the estimation of 3D human poses has important application value in virtual reality, medical diagnosis, athlete training and other fields. However, human pose recovery and retargeting req...

A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer.

Scientific data
The success of training computer-vision models heavily relies on the support of large-scale, real-world images with annotations. Yet such an annotation-ready dataset is difficult to curate in pathology due to the privacy protection and excessive anno...

Privacy-preserving artificial intelligence in healthcare: Techniques and applications.

Computers in biology and medicine
There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very ...