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[Design and implementation of a clinical trial image medical record desensitization system based on artificial intelligence technology]

Objective: To designe a clinical trial image medical record desensitization system based on artificial intelligence technology and to conduct preliminary applications, achieving the protection of patient privacy and promoting the safe use of clinical image data in clinical trial projects. Methods: The clinical trial image record desensitization system based on artificial intelligence technology was first performed to preprocess operations, such as cropping and compression on medical images to initialize the imag…

By the Truvace desk2 min read1 sourcePeer-reviewedTRV-2026-1530· cite & verify
[Design and implementation of a clinical trial image medical record desensitization system based on artificial intelligence technology]
Hospital Universitari Doctor Peset, València 09 by 19Tarrestnom65. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0

In brief

Researchers designed a system that uses image recognition, natural language processing and convolutional neural networks to classify medical documents in clinical trial image records. It finds personal identifiers such as names, ID numbers, medical record numbers, phone numbers and birthplaces, then applies pixel-level masking or blurring.

After the system processed 100,000 image records, 9,000 samples were randomly chosen to check the automated desensitization. The authors say this supports privacy protection and safer sharing of trial image data. Accuracy varied by document type, and the abstract reports the system is paired with a manual online step.

Main points

  1. The pipeline first preprocesses images by cropping and compressing them before classification and text recognition.
  2. Targeted identifiers include patient name, identification number, medical record number, phone number and birthplace.
  3. Verification used 9,000 randomly selected samples from 100,000 processed image records.
  4. Average accuracy was 88.48%, rising to 99.40% for serious adverse event documents.
  5. A canvas-based manual online desensitization system assists the automated step.

The gain

An AI system automatically finds and masks patient identifiers in clinical trial image records, reaching 88.48% average accuracy and 99.40% on serious adverse event documents in sampled checks.

The rundown

The system starts by preprocessing images, then classifies document content through labeling, feature extraction and text recognition. Sensitive points it finds are masked or blurred at pixel level.

For validation, 100,000 image records were desensitized and 9,000 samples from the processed document types were randomly selected for automated effect verification. Average accuracy across document types was 88.48%.

Performance was strongest for serious adverse event medical documents, at up to 99.40%. The authors conclude the tool, combined with the manual online auxiliary system, can be used in production environments for safe sharing of trial image records.

Sources

  1. Peer-reviewedBeijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences2026-10-18

The debate