4 research outputs found
PACS and Electronic Health Records
Electronic Health Record (EHR) is a major component of the health informatics domain. An important part of the EHR is the medical images obtained over a patientâs lifetime and stored in diverse PACS. The vision presented in this paper is that future medical information systems will convert data from various medical sources â including diverse modalities, PACS, HIS, CIS, RIS, and proprietary systems â to HL7 standard XML documents. Then, the various documents are indexed and compiled to EHRs, upon which complex queries can be posed. We describe the conversion of data retrieved from PACS systems through DICOM to HL7 standard XML documents. This enables the EHR system to answer queries such as âGet all chest images of patients at the age of 20-30, that have blood type âA â and are allergic to pine treesâ, which a single PACS cannot answer. The integration of data from multiple sources makes our approach capable of delivering such answers. It enables the correlation of medical, demographic, clinical, and even genetic information. In addition, by fully indexing all the tagged data in DICOM objects, it becomes possible to offer access to huge amounts of valuable data, which can be better exploited in the specific radiology domain
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A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis
Importance: An accurate and robust artificial intelligence (AI) algorithm for detecting cancer in digital breast tomosynthesis (DBT) could significantly improve detection accuracy and reduce health care costs worldwide.Objectives: To make training and evaluation data for the development of AI algorithms for DBT analysis available, to develop well-defined benchmarks, and to create publicly available code for existing methods.Design, Setting, and Participants: This diagnostic study is based on a multi-institutional international grand challenge in which research teams developed algorithms to detect lesions in DBT. A data set of 22032 reconstructed DBT volumes was made available to research teams. Phase 1, in which teams were provided 700 scans from the training set, 120 from the validation set, and 180 from the test set, took place from December 2020 to January 2021, and phase 2, in which teams were given the full data set, took place from May to July 2021.Main Outcomes and Measures: The overall performance was evaluated by mean sensitivity for biopsied lesions using only DBT volumes with biopsied lesions; ties were broken by including all DBT volumes.Results: A total of 8 teams participated in the challenge. The team with the highest mean sensitivity for biopsied lesions was the NYU B-Team, with 0.957 (95% CI, 0.924-0.984), and the second-place team, ZeDuS, had a mean sensitivity of 0.926 (95% CI, 0.881-0.964). When the results were aggregated, the mean sensitivity for all submitted algorithms was 0.879; for only those who participated in phase 2, it was 0.926.Conclusions and Relevance: In this diagnostic study, an international competition produced algorithms with high sensitivity for using AI to detect lesions on DBT images. A standardized performance benchmark for the detection task using publicly available clinical imaging data was released, with detailed descriptions and analyses of submitted algorithms accompanied by a public release of their predictions and code for selected methods. These resources will serve as a foundation for future research on computer-assisted diagnosis methods for DBT, significantly lowering the barrier of entry for new researchers.</p