Scotland has more than five million people within a single health system, using a single central Picture Archiving and Commication System (PACS) for radiography data. This enabled the project team to build a research resource exceeding a petabyte of imaging from 2010 onwards, with open-source tooling to collate and de-identify images on demand. Image metadata, treatment and diagnostic records can be used to define large cohorts of patients then make the data available remotely to researchers in a Trusted Research Environment (TRE). Original identifiable images are stored in one of three isolated zones with controlled data transfer to protect patient privacy and prevent inadvertent disclosure. Linkage to other records is performed in the second zone on de-identified images using encrypted patient identifiers. Automated screening with optical character recognition and natural language processing was implemented to identify and redact personally identifiable information before release to researchers in the third zone. A recent extension to this system has provided an ongoing feed of routine imaging, which is securely shared with regional counterparts to ensure the minimal possible additional load is placed on clinical PACS resources and avoid duplicate requests. The project launched in April 2022 and since then a variety of research projects have already used this environment and data representing millions of pounds of funding, some using large cohorts with historical data up to 14 years old, re-assessing historical scans with the benefit of subsequent diagnosis to investigate possible early warning signs of conditions including dementia and pre-cancerous lung nodules.

Keywords: MRI, Imaging Sequences, Ultrasound, Mammography, CT, Angiography, Conventional Radiography Published under a CC BY 4.0 license. See also the commentary by Whitman and Vining in this issue.

ObjectivesTo research and develop tools and methods for building cohorts of images linked to longitudinal healthcare records from real-world clinical images from the whole Scottish population. To provide this capability for the Scottish Medical Imaging service (provided by the Scottish National Safe Haven) to support research and AI projects. ApproachClinical images, especially when linked to routinely collected health data, are extremely useful for many types of research and AI development. However, finding and using clinical images for research data is challenging because: 1) Existing software used to search for images are designed for clinical care rather than research making it easy to find images for a particular patient. They are not designed to search for all images with particular characteristics e.g. slice thickness/scanning protocol/contrast agent/patient medication. 2) Reuse of clinical images for research requires de-identification, yet identifiable data can be present in many areas of the associated image file. ResultsThe PICTURES (InterdisciPlInary Collaboration for efficienT and effective Use of clinical images in big data health care RESearch) 5-year programme has developed an architecture for building cohorts of images based upon research criteria and providing these in a di-identifiable form within a Safe Haven environment. There are 3 zones: An identifiable zone which stores the raw image data and a MongoDB database which captures the metadata A de-identified zone which provides a database and tools for cohort building which do not require imaging data expertise Several Project Private Zones (PPZs) where researchers can install custom software and access the de-identified images for their project The architecture supports cohort building based upon features within pixel data, image metadata and linking to longitudinal health care records. ConclusionPICTURES is currently enhancing the cohort building user interface used by the National Safe Haven and supporting exemplar projects. The SMI service is live and accepting requests for more information. The software is open source and we welcome the use of the platform by other Safe Havens/research groups.

BACKGROUND:A Trusted Research Environment (TRE; also known as a Safe Haven) is an environment supported by trained staff and agreed processes (principles and standards), providing access to data for research while protecting patient confidentiality. Accessing sensitive data without compromising the privacy and security of the data is a complex process. OBJECTIVE:This paper presents the security measures, administrative procedures, and technical approaches adopted by TREs. METHODS:We contacted 73 TRE operators, 22 (30%) of whom, in the United Kingdom and internationally, agreed to be interviewed remotely under a nondisclosure agreement and to complete a questionnaire about their TRE. RESULTS:We observed many similar processes and standards that TREs follow to adhere to the Seven Safes principles. The security processes and TRE capabilities for supporting observational studies using classical statistical methods were mature, and the requirements were well understood. However, we identified limitations in the security measures and capabilities of TREs to support "next-generation" requirements such as wide ranges of data types, ability to develop artificial intelligence algorithms and software within the environment, handling of big data, and timely import and export of data. CONCLUSIONS:We found a lack of software or other automation tools to support the community and limited knowledge of how to meet the next-generation requirements from the research community. Disclosure control for exporting artificial intelligence algorithms and software was found to be particularly challenging, and there is a clear need for additional controls to support this capability within TREs.

Aim To enable a world-leading research dataset of routinely collected clinical images linked to other routinely collected data from the whole Scottish national population. This includes more than 30 million different radiological examinations from a population of 5.4 million and >2 PB of data collected since 2010. Methods Scotland has a central archive of radiological data used to directly provide clinical care to patients. We have developed an architecture and platform to securely extract a copy of those data, link it to other clinical or social datasets, remove personal data to protect privacy, and make the resulting data available to researchers in a controlled Safe Haven environment. Results An extensive software platform has been developed to host, extract, and link data from cohorts to answer research questions. The platform has been tested on 5 different test cases and is currently being further enhanced to support 3 exemplar research projects. Conclusions The data available are from a range of radiological modalities and scanner types and were collected under different environmental conditions. These real-world, heterogenous data are valuable for training algorithms to support clinical decision making, especially for deep learning where large data volumes are required. The resource is now available for international research access. The platform and data can support new health research using artificial intelligence and machine learning technologies, as well as enabling discovery science.

The frequency and effects of exchange transfusion in a large number of prospectively studied neonates in the National Institute of Child Health and Human Development (NICHD) phototherapy study of 1974 to 1976, who were randomly assigned to receive phototherapy or not, are reported here.18 Previously published studies14,52,78,102 on the morbidity and mortality rates of exchange transfusion are based upon experience accumulated prior to 1970. MATERIALS AND METHODS A total of 190 patients received 331 exchange transfusions. Data concerning each exchange transfusion were collected and analyzed by the NICHD Phototherapy Study Statistical Center. Statistical comparisons were done by X2 and Student9s t test when appropriate. A narrative description of events surrounding an exchange transfusion was obtained if morbidity requiring clarification was reported, such as a cyanotic episode associated with the procedure. Additional information was obtained from the postmortem report on every infant who received an exchange transfusion and died, and for whom an autopsy was performed. All centers used fresh donor blood anticoagulated with a solution of citrate, phosphate, and dextrose. Patients in two centers routinely received small doses of calcium gluconate periodically during the procedure. Calcium gluconate was given to the others only if tachycardia or unusual irritability developed. All procedures were performed using modifications of the technique described by Allen and Diamond.2 Aliquots of blood used in the exchange ranged from 5 to 20 mL; the smaller aliquots were used in the low-birth-weight patients. Indications for exchange transfusion by birth weight, serum bilirubin concentration, and other clinical indices of risk were contained in the study protocol.

THE IMPORTANCE of vitamin K in the prevention of neonatal bleeding is the subject of debate and uncertainty. Doubt has led to vacillating practice: various preparations are used, various doses are administered; indeed, various opinions are expressed with respect to the need for any vitamin K at all. Lying-in services where little hemorrhagic disease is observed and where vitamin K is not given, tend to draw patients from higher socioeconomic groups than services where hemorrhagic disease of the newborn is a significant clinical entity. For example, recent clinical studies demonstrating a high incidence of the disease are reported from Cuba,1Tennessee,2and Texas.3These studies are from services where the patients are predominantly medically indigent and from lower socioeconomic groups. The present studies were undertaken to define the frequency of hemorrhagic disease of the newborn in the medically indigent population of Cincinnati; to redefine an effective prophylactic