Enhancing Prostate Cancer Diagnosis with a Novel Artificial Intelligence-Based Web Application: Synergizing Deep Learning Models, Multimodal Data, and Insights from Usability Study with Pathologists.
Akarsh SinghShruti RandiveAnne BreggiaBilal AhmadRobert ChristmanSaeed AmalPublished in: Cancers (2023)
Prostate cancer remains a significant cause of male cancer mortality in the United States, with an estimated 288,300 new cases in 2023. Accurate grading of prostate cancer is crucial for ascertaining disease severity and shaping treatment strategies. Modern deep learning techniques show promise in grading biopsies, but there is a gap in integrating these advances into clinical practice. Our web platform tackles this challenge by integrating human expertise with AI-driven grading, incorporating diverse data sources. We gathered feedback from four pathologists and one medical practitioner to assess usability and real-world alignment through a survey and the NASA TLX Usability Test. Notably, 60% of users found it easy to navigate, rating it 5.5 out of 7 for ease of understanding. Users appreciated self-explanatory information in popup tabs. For ease of use, all users favored the detailed summary tab, rating it 6.5 out of 7. While 80% felt patient demographics beyond age were unnecessary, high-resolution biopsy images were deemed vital. Acceptability was high, with all users willing to adopt the app, and some believed it could reduce workload. The NASA TLX Usability Test indicated a low-moderate perceived workload, suggesting room for improved explanations and data visualization.
Keyphrases
- artificial intelligence
- deep learning
- prostate cancer
- big data
- electronic health record
- health information
- radical prostatectomy
- machine learning
- high resolution
- convolutional neural network
- clinical practice
- healthcare
- endothelial cells
- depressive symptoms
- mental health
- social support
- papillary thyroid
- drinking water
- risk factors
- case report
- social media
- high throughput
- optical coherence tomography
- mass spectrometry
- physical activity
- young adults
- cardiovascular disease
- psychometric properties
- type diabetes
- fine needle aspiration