Design and Development of a Web-Based Prospective Nationwide Registry for Ocular Inflammatory Diseases: UVEITE.PT - The Portuguese Ocular Inflammation Registry

Uveitis is a heterogeneous collection of infrequent diseases, which poses significant challenges to cost-effective research in the field. Medical registries are being increasingly recognized as crucial tools to provide high-quality data, thus enabling prospective clinical research. This paper describes the design and technical structure development of an innovative countrywide electronic medical record for uveitis, Uveite.pt, and gives an overview of the cohort registered since its foundation, March 2020.Uveite.pt is an electronic medical record platform developed by the Portuguese Ocular Inflammation Group (POIG), a scientific committee of the Portuguese Ophthalmology Society. This is a nationwide customized web-based platform for uveitis patients useful for both clinical practice and real-world-based research, working as a central repository and reporting tool for uveitis. This paper describes the technical principles, the design and the development of a web-based interoperable registry for uveitis in Portugal and provides an overview of more than 400 patients registered in the first 18 months since inception.In infrequent diseases, the existence of registries enables to gather evidence and increase research possibilities to clinicians. The adoption of this platform enables standardization and improvement of clinical practice in uveitis. It is useful to apprehend the repercussion of medical and surgical treatments in uveitis and scleritis, supporting clinicians in the strict monitoring of drug adverse reactions and surgical outcomes.

Enhanced sleep staging with artificial intelligence: a validation study of new software for sleep scoring

Manual sleep staging (MSS) using polysomnography is a time-consuming task, requires significant training, and can lead to significant variability among scorers. STAGER is a software program based on machine learning algorithms that has been developed by Medibio Limited (Savage, MN, USA) to perform automatic sleep staging using only EEG signals from polysomnography. This study aimed to extensively investigate its agreement with MSS performed during clinical practice and by three additional expert sleep technicians. Forty consecutive polysomnographic recordings of patients referred to three US sleep clinics for sleep evaluation were retrospectively collected and analyzed. Three experienced technicians independently staged the recording using the electroencephalography, electromyography, and electrooculography signals according to the American Academy of Sleep Medicine guidelines. The staging initially performed during clinical practice was also considered. Several agreement statistics between the automatic sleep staging (ASS) and MSS, among the different MSSs, and their differences were calculated. Bootstrap resampling was used to calculate 95% confidence intervals and the statistical significance of the differences. STAGER's ASS was most comparable with, or statistically significantly better than the MSS, except for a partial reduction in the positive percent agreement in the wake stage. These promising results indicate that STAGER software can perform ASS of inpatient polysomnographic recordings accurately in comparison with MSS.

The need for action by evaluators and decision makers in Europe to ensure safe use of medical software

Digital Health Solutions (DHS) approved under the Medical Device Directive (MDD) in the European Union may be used until May 27, 2025. The regulation provides appropriate requirements for the products but lack the evaluation by an external independent organization. For many DHS, the company can make a self-certification that the requirements have been fulfilled. As demonstrated in the evaluation of smartphone-based apps for skin cancer risk assessment such products may expose the public to undue risks. The new Medical Device Regulation provides adequate control of DHS through evaluation of independent organization prior to allowing the product on the market. HTA-evaluators and those who make decisions regarding the use of DHS need to understand the associated risks with the use of products approved according to the MDD and ensure appropriate risk mitigations to ensure that the public is not exposed to undue risk. This perspective aims to inform decisionmakers about the risks associated with the delayed requirement to transition to the new MDR regulation. There is a gap in the current guidance regarding the evolving use of machine learning and artificial intelligence. With the evolving use of DHS, it is important that industry, regulators and HTA evaluators work jointly to establish the safe and effective use of DHS.

Methodology for Conducting Post-Marketing Surveillance of Software as a Medical Device Based on Artificial Intelligence Technologies

The aim of the study was to develop a methodology for conducting post-registration clinical monitoring of software as a medical device based on artificial intelligence technologies (SaMD-AI).

Materials and Methods

The methodology of post-registration clinical monitoring is based on the requirements of regulatory legal acts issued by the Board of the Eurasian Economic Commission. To comply with these requirements, the monitoring involves submission of the review of adverse events reports, the review of developers' routine reports on the safety and efficiency of SaMD-AI, and the assessment of the system for collecting and analyzing developers' post-registration data on the safety and efficiency of medical devices. The methodology was developed with regard to the recommendations of the International Medical Device Regulators Forum and the documents issued by the Food and Drug Administration (USA). Field-testing of this methodology was carried out using SaMD-AI designed for diagnostic imaging.

Results

The post-registration monitoring of SaMD-AI consists of three key stages: collecting user feedback, technical monitoring and clinical validation. Technical monitoring involves routine evaluation of SaMD-AI output data quality to detect and remove flaws in a timely manner, and to secure the product stability. Major outcomes include an ordered list of technical flaws in SaMD-AI and their classification using evidence from diagnostic imaging studies. The application of this methodology resulted in a gradual reduction in the number of studies with flaws due to timely improvements in artificial intelligence algorithms: the number of flaws decreased to 5% in various aspects during subsequent testing. Clinical validation confirmed that SaMD-AI is capable of producing clinically meaningful outputs related to its intended use within the functionality determined by the developer. The testing procedure and the baseline testing framework were established during the field testing.

Conclusion

The developed methodology will ensure the safety and efficiency of SaMD-AI taking into account its specifics as intangible medical devices. The methodology presented in this paper can be used by SaMD-AI developers to plan and carry out the post-registration clinical monitoring.

Automated assessment of joint synovitis activity from medical ultrasound and power doppler examinations using image processing and machine learning methods

Objectives

Rheumatoid arthritis is the most common rheumatic disease with arthritis, and causes substantial functional disability in approximately 50% patients after 10 years. Accurate measurement of the disease activity is crucial to provide an adequate treatment and care to the patients.

The aim of this study is focused on a computer aided diagnostic system that supports an assessment of synovitis severity.

Material and methods

This paper focus on a computer aided diagnostic system that was developed within joint Polish–Norwegian research project related to the automated assessment of the severity of synovitis. Semiquantitative ultrasound with power Doppler is a reliable and widely used method of assessing synovitis. Synovitis is estimated by ultrasound examiner using the scoring system graded from 0 to 3. Activity score is estimated on the basis of the examiner’s experience or standardized ultrasound atlases. The method needs trained medical personnel and the result can be affected by a human error.

Results

The porotype of a computer-aided diagnostic system and algorithms essential for an analysis of ultrasonic images of finger joints are main scientific output of the MEDUSA project. Medusa Evaluation System prototype uses bone, skin, joint and synovitis area detectors for mutual structural model based evaluation of synovitis. Finally, several algorithms that support the semi-automatic or automatic detection of the bone region were prepared as well as a system that uses the statistical data processing approach in order to automatically localize the regions of interest.

Conclusions

Semiquantitative ultrasound with power Doppler is a reliable and widely used method of assessing synovitis. Activity score is estimated on the basis of the examiner’s experience and the result can be affected by a human error. In this paper we presented the MEDUSA project which is focused on a computer aided diagnostic system that supports an assessment of synovitis severity.

Development and validation of a computer application to aid the physician's decision-making process at the start of and during treatment with insulin in type 2 diabetes: a randomized and controlled trial

BACKGROUND

Achieving optimum blood glucose control in patients with type 2 diabetes mellitus (T2DM) is difficult. Some primary care physicians (PCPs) delay the start of insulin use because of the uncertainty in intensifying insulin therapy. The objective was to develop and validate a computer application (CA) that helps PCPs to make decisions about insulin therapy in order to achieve a significant improvement in glycated hemoglobin (HbA1c).

METHODS

This was a cluster-randomized clinical trial. Fourteen primary care centers (PCCs) in Madrid with 66 PCPs and 697 T2DM patients on insulin therapy were randomly divided into two groups of seven PCCs each. In the intervention group, seven PCCs included 39 PCPs and 365 T2DM patients on insulin therapy. These PCPs were free to use the CA. A further seven PCCs were assigned to the control group with 27 PCPs and 332 T2DM patients on insulin therapy. The control group did not use the CA. The duration of the trial was 18 months to validate the CA. The outcome was a change in HbA1c from baseline.

RESULTS

In the intervention group, the final HbA1c was 7.19% (standard deviation [SD] ± 0.93), with a difference from the start of -0.69% (p = .001). In the control group, it was 7.71% (SD ± 1.37), with a difference from the start of -0.09% (p not significant).

CONCLUSIONS

This CA helps to improve HbA1c figures of T2DM patients with insulin when it is used by PCPs to make decisions when starting, continuing, or changing insulin and its dosage.