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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Automatic decision using dirty databases: application to prostate cancer diagnosis.

Oscar R Marin¹, Daniel Ruiz, Antonio Soriano

¹Bioinspired Engineering and Health Computing Research Group, University of Alicante, P.O. 99 E-03080 Spain. omarin@ibisrg.com

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 25, 2010

Summary

This summary is machine-generated.

Early cancer detection improves survival rates. This study shows artificial neural networks can maintain diagnostic accuracy even with imperfect clinical data, overcoming data integrity issues in prostate cancer diagnosis.

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Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

Area of Science:

Oncology
Medical Informatics
Artificial Intelligence

Background:

Early cancer detection and treatment are crucial for reducing mortality.
Clinical decision support systems (CDSS) aid early diagnosis but are sensitive to data quality.
Clinical databases often contain 'dirty data,' including missing values and inconsistent formatting, impacting CDSS performance.

Purpose of the Study:

To evaluate the performance of an artificial neural network-based CDSS using both clean and dirty prostate cancer datasets.
To determine if CDSS performance can be maintained despite data integrity issues.

Main Methods:

A clinical decision support system utilizing artificial neural networks was developed.
The system was tested on curated datasets of prostate cancer data, including both 'clean' (error-free) and 'dirty' (containing common data integrity issues) sets.
Performance metrics were compared between the clean and dirty data scenarios.

Main Results:

The study demonstrated that the developed implementation effectively handles data integrity problems.
The artificial neural network-based CDSS achieved comparable performance when using both clean and dirty datasets.
The system successfully mitigated the negative impact of non-standard or missing data.

Conclusions:

It is feasible to implement CDSS that are robust to common data integrity issues found in clinical databases.
Artificial neural networks can provide reliable prostate cancer diagnosis support even with imperfect data.
This approach enhances the practical utility of automated diagnostic tools in real-world clinical settings.