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.

artificial intelligence biopsy grading clinical validation digital pathology human computer interaction multimodal data prostate cancer usability test

Journal

Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829

Informations de publication

Date de publication:
30 Nov 2023
Historique:
received: 21 09 2023
revised: 22 11 2023
accepted: 23 11 2023
medline: 9 12 2023
pubmed: 9 12 2023
entrez: 9 12 2023
Statut: epublish

Résumé

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.

Identifiants

pubmed: 38067363
pii: cancers15235659
doi: 10.3390/cancers15235659
pmc: PMC10705310
pii:
doi:

Types de publication

Journal Article

Langues

eng

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Auteurs

Akarsh Singh (A)

College of Engineering, Northeastern University, Boston, MA 02115, USA.

Shruti Randive (S)

College of Engineering, Northeastern University, Boston, MA 02115, USA.

Anne Breggia (A)

Maine Health Institute for Research, Scarborough, ME 04074, USA.

Bilal Ahmad (B)

Maine Medical Center, Portland, ME 04102, USA.

Robert Christman (R)

Maine Medical Center, Portland, ME 04102, USA.

Saeed Amal (S)

The Roux Institute, Department of Bioengineering, College of Engineering, Northeastern University, Boston, MA 02115, USA.

Classifications MeSH