A Data Ingestion Procedure towards a Medical Images Repository

Author:

Solar Mauricio1ORCID,Castañeda Victor2ORCID,Ñanculef Ricardo3,Dombrovskaia Lioubov3,Araya Mauricio4ORCID

Affiliation:

1. Departamento de Informática, Universidad Tecnica Federico Santa Maria, Campus Vitacura-Santiago, Vitacura 7660251, Chile

2. DETEM, Faculty of Medicine, Universidad de Chile, Independencia-Santiago, Santiago 8380453, Chile

3. Departamento de Informática, Universidad Tecnica Federico Santa Maria, Campus San Joaquin-Santiago, Santiago 8940897, Chile

4. Departamento de Informática, Universidad Tecnica Federico Santa Maria, Campus Casa Central-Valparaíso, Valparaíso 2390123, Chile

Abstract

This article presents an ingestion procedure towards an interoperable repository called ALPACS (Anonymized Local Picture Archiving and Communication System). ALPACS provides services to clinical and hospital users, who can access the repository data through an Artificial Intelligence (AI) application called PROXIMITY. This article shows the automated procedure for data ingestion from the medical imaging provider to the ALPACS repository. The data ingestion procedure was successfully applied by the data provider (Hospital Clínico de la Universidad de Chile, HCUCH) using a pseudo-anonymization algorithm at the source, thereby ensuring that the privacy of patients’ sensitive data is respected. Data transfer was carried out using international communication standards for health systems, which allows for replication of the procedure by other institutions that provide medical images. Objectives: This article aims to create a repository of 33,000 medical CT images and 33,000 diagnostic reports with international standards (HL7 HAPI FHIR, DICOM, SNOMED). This goal requires devising a data ingestion procedure that can be replicated by other provider institutions, guaranteeing data privacy by implementing a pseudo-anonymization algorithm at the source, and generating labels from annotations via NLP. Methodology: Our approach involves hybrid on-premise/cloud deployment of PACS and FHIR services, including transfer services for anonymized data to populate the repository through a structured ingestion procedure. We used NLP over the diagnostic reports to generate annotations, which were then used to train ML algorithms for content-based similar exam recovery. Outcomes: We successfully implemented ALPACS and PROXIMITY 2.0, ingesting almost 19,000 thorax CT exams to date along with their corresponding reports.

Funder

Chilean National Commission for Scientific and Technological Research projects IDEA FONDEF

ANID-Basal Project

FONDEQUIP

joint project UTFSM-CASSACA

Publisher

MDPI AG

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