CIMIDx: Prototype for a Cloud-Based System to Support Intelligent Medical Image Diagnosis With Efficiency

Area of Science:

• Medical Informatics
• Oncology
• Cloud Computing

Background:

• The internet and cloud computing offer new avenues for healthcare delivery.
• A gap exists in fully automated, cloud-based self-management interventions for breast cancer patients.
• Practical guidelines for such interventions are currently lacking.

Purpose of the Study:

• To investigate the prevalence and predictors of cloud use for medical diagnosis among women with breast cancer.
• To evaluate a generic, fully automated cloud-based self-intervention model using a prototype called CIMIDx.
• To assess how breast cancer survivors utilize a self-management model for medical image diagnosis.

Main Methods:

• Developed and deployed the Cloud-Based System Support Intelligent Medical Image Diagnosis (CIMIDx) prototype in the cloud.
• Utilized two modules for training/testing cloud services and analyzing classification accuracy using client data.
• Monitored cloud server requests for usage statistics and evaluated end-user experience with browsing functionalities.

Main Results:

• The CIMIDx prototype demonstrated high diagnostic performance, achieving up to 99.29% sensitivity and 98% accuracy in detecting breast health issues.
• Intervention adherence rate was 44.3% among 114 surveyed breast cancer survivors.
• Participants reported favorable usefulness, satisfaction, and ease of navigation, supporting the prototype's efficacy.

Conclusions:

• Women responded positively to the generic, fully automated cloud-based self-management prototype (CIMIDx).
• The CIMIDx prototype improved cancer detection rates in screening and diagnosis.
• The study highlights the potential of cloud-based systems for enhanced breast cancer management and diagnosis.