Scoping review of clinical decision support systems for multiple sclerosis management: Leveraging information technology and massive health data.

BACKGROUND AND PURPOSE: Multiple sclerosis (MS) is a complex autoimmune disease of the central nervous system, with numerous therapeutic options, but a lack of biomarkers to support a mechanistic approach to precision medicine. A computational approach to precision medicine could proceed from clinical decision support systems (CDSSs). They are digital tools aiming to empower physicians through the clinical applications of information technology and massive data. However, the process of their clinical development is still maturing; we aimed to review it in the field of MS. METHODS: For this scoping review, we screened systematically the PubMed database. We identified 24 articles reporting 14 CDSS projects and compared their technical and software development aspects. RESULTS: The projects position themselves in various contexts of usage with various algorithmic approaches: expert systems, CDSSs based on similar patients' data visualization, and model-based CDSSs implementing mathematical predictive models. So far, no project has completed its clinical development up to certification for clinical use with global release. Some CDSSs have been replaced at subsequent project iterations. The most advanced projects did not necessarily report every step of clinical development in a dedicated article (proof of concept, offline validation, refined prototype, live clinical evaluation, comparative prospective evaluation). They seek different software distribution options to integrate into health care: internal usage, "peer-to-peer," and marketing distribution. CONCLUSIONS: This review illustrates the potential of clinical applications of information technology and massive data to support MS management and helps clarify the roadmap for future projects as a multidisciplinary and multistep process.

Multiple sclerosis clinical decision support system based on projection to reference datasets.

OBJECTIVE: Multiple sclerosis (MS) is a multifactorial disease with increasingly complicated management. Our objective is to use on-demand computational power to address the challenges of dynamically managing MS. METHODS: A phase 3 clinical trial data (NCT00906399) were used to contextualize the medication efficacy of peg-interferon beta-1a vs placebo on patients with relapsing-remitting MS (RRMS). Using a set of reference patients (PORs), selected based on adequate features similar to those of an individual patient, we visualize disease activity by measuring the percentage of relapses, accumulation of new T2 lesions on MRI, and worsening EDSS during the clinical trial. RESULTS: We developed MS Vista, a functional prototype of clinical decision support system (CDSS), with a user-centered design and distributed infrastructure. MS Vista shows the medication efficacy of peginterferon beta-1a versus placebo for each individual patient with RRMS. In addition, MS Vista initiated the integration of a longitudinal magnetic resonance imaging (MRI) viewer and interactive dual physician-patient data display to facilitate communication. INTERPRETATION: The pioneer use of PORs for each individual patient enables personalized analytics sustaining the dialog between neurologists, patients and caregivers with quantified evidence.

Addressing the Challenges and Barriers to the Integration of Machine Learning into Clinical Practice: An Innovative Method to Hybrid Human-Machine Intelligence.

Machine learning (ML) models have proven their potential in acquiring and analyzing large amounts of data to help solve real-world, complex problems. Their use in healthcare is expected to help physicians make diagnoses, prognoses, treatment decisions, and disease outcome predictions. However, ML solutions are not currently deployed in most healthcare systems. One of the main reasons for this is the provenance, transparency, and clinical utility of the training data. Physicians reject ML solutions if they are not at least based on accurate data and do not clearly include the decision-making process used in clinical practice. In this paper, we present a hybrid human-machine intelligence method to create predictive models driven by clinical practice. We promote the use of quality-approved data and the inclusion of physician reasoning in the ML process. Instead of training the ML algorithms on the given data to create predictive models (conventional method), we propose to pre-categorize the data according to the expert physicians' knowledge and experience. Comparing the results of the conventional method of ML learning versus the hybrid physician-algorithm method showed that the models based on the latter can perform better. Physicians' engagement is the most promising condition for the safe and innovative use of ML in healthcare.

Automated visibility map of the internal colon surface from colonoscopy video.

PURPOSE: Optical colonoscopy is a prominent procedure by which clinicians examine the surface of the colon for cancerous polyps using a flexible colonoscope. One of the main concerns regarding the quality of the colonoscopy is to ensure that the whole colonic surface has been inspected for abnormalities. In this paper, we aim at estimating areas that have not been covered thoroughly by providing a map from the internal colon surface. METHODS: Camera parameters were estimated using optical flow between consecutive colonoscopy frames. A cylinder model was fitted to the colon structure using 3D pseudo stereo vision and projected into each frame. A circumferential band from the cylinder was extracted to unroll the internal colon surface (band image). By registering these band images, drift in estimating camera motion could be reduced, and a visibility map of the colon surface could be generated, revealing uncovered areas by the colonoscope. Hidden areas behind haustral folds were ignored in this study. The method was validated on simulated and actual colonoscopy videos. The realistic simulated videos were generated using a colonoscopy simulator with known ground truth, and the actual colonoscopy videos were manually assessed by a clinical expert. RESULTS: The proposed method obtained a sensitivity and precision of 98 and 96 % for detecting the number of uncovered areas on simulated data, whereas validation on real videos showed a sensitivity and precision of 96 and 78 %, respectively. Error in camera motion drift could be reduced by almost 50 % using results from band image registration. CONCLUSION: Using a simple cylindrical model for the colon and reducing drift by registering band images allows for the generation of visibility maps. The current results also suggest that the provided feedback through the visibility map could enhance clinicians' awareness of uncovered areas, which in return could reduce the probability of missing polyps.

Development of a novel image-based program to teach narrow-band imaging.

OBJECTIVES: Narrow-band imaging (NBI) is a widely available endoscopic imaging technology; however, uptake of the technique could be improved. Teaching new imaging techniques and assessing trainees' performance can be a challenging exercise during a 1-day workshop. To support NBI training, we developed an online training tool (Medimq) to help experts train novices in NBI bronchoscopy that could assess trainees' performance and provide feedback before the close of the 1-day course. The present study determines whether trainees' capacity to identify relevant pathology increases with the proposed interactive testing method. METHODS: Two groups of 20 and 18 bronchoscopists have attended an NBI course where they did a pretest and post-test before and after the main lecture, and a follow-up test 4 weeks later to measure retention of knowledge. We measured their ability to mark normal and abnormal 'biopsy size' areas on bronchoscopic NBI images for biopsy. These markings were compared with areas marked by experts on the same images. RESULTS: The first group results were used to pilot the test. After modifications, the results of the improved test for group 2 showed trainees improved by 32% (total class average normalized gain) in detecting normal or abnormal areas. On follow-up testing, Group 2 improved by 23%. CONCLUSIONS: The overall class average normalized gain of 32% shows our test can be used to improve trainees' competency in analyzing NBI Images. The testing method (and tool) can be used to measure the follow up 4 weeks later. Better follow-up test results would be expected with more frequent practice by trainees after the course.

Outside Observer, an Enhanced Training Methodology: Bringing Back the Expert's Eye whilst Training Alone.

Traditional surgical teaching requires an expert trainer to be present during training. This training model allows an expert to observe a novice's performance and provide corrective feedback as errors are made. Virtual simulations offer a much needed alternative to learning through error and allow the trainee to hone skills without the presence of a skilled surgical trainer. However, current simulations limit the scope of training to tool directed learning, focusing training around instrumented simulated tool tips. This paper identifies a need for trainee centered virtual training to complement current approaches. The Outside Observer training methodology provides training feedback based on the observation of surgical tool tip manipulations in conjunction with the external movements a trainee performs during a procedure. It is thought that integrating movement and posture training with current tool tracking methods will provide more effective and accelerated learning.

Innovating across disciplines: report on a national workshop of stakeholders in Australia.

The most effective innovations in healthcare require the input of multidisciplinary teams working from white board to bedside. Innovations must ultimately deliver tangible results in the real world. The skills required at each stage of the development from drawing board to bench top and from the lab to the clinic may be entirely unrelated. The key results at each stage also vary depending on perspective; they may be acclaim and awards, sales and profits or improved clinical parameters. As teams are enlisted on a specific challenge they each focus primarily on their own key performance indicators. In this paper we report the deliberations at a workshop involving a variety of disciplines working in healthcare. The participants emphasised the need for clear agreement on three aspects: the outputs of the project including the financial and intellectual property rights; the risks, costs and benefits; and the timelines for completion. A lead organisation must broker and maintain relationships ideally facilitated by an experienced project manager. The greatest challenges were highlighted as: the return on investment for commercial partners; the timelines for academic outputs; and the potential for disruption of clinical practice routines.