Medical image recognition through Deep Learning algorithms

Data Science Methods

Deep Learning is a machine learning algorithm often used in image recognition. This method reduces a medical image (MRI, CT, plain radiographs) to a numeric matrix, and an algorithm then analyzes this matrix in search of certain features associated with specific radiologic signs and diagnosis. It presents several advantages over the traditional way of image classification by clinical experts. Below we outline the comparison between the two, how the algorithm works, and what you would need if you would like to create a Deep Learning algorithm for your image recognition task. [Read More]

Bayesian adaptive trials

Data Science Methods

Bayesian adaptive trials are often used in comparative effectiveness research, and allow for modifying trial design features based on the data collected throughout the study. This design offers multiple advantages over conventional trials in which modifications are not generally allowed during enrollment and follow-up of patients. Below we outline the comparison between traditional versus adaptive trial designs, providing an overview of the adaptive trial implementation, and basic requirements for implementing it. [Read More]

Remote data collection at SporeData.

SporeData services

In contrast with a Contract Research Organization (CRO), SporeData’s primary mission is related to Data Science applied to clinical and healthcare policy research. This difference means that we are usually not in charge of collecting patient data on-site. With that said, remote data collection is part of our portfolio. Below re some examples. Email surveys. We frequently prepare the design and conduct or email surveys, including those to provide population presentation, i. [Read More]

Data reports

Novel Designs

We provide a range of data-driven report formats so that you can communicate your results to different research stakeholders. Below we outlined some of the key aspects of our workflow: Use cases. Our data-driven reports cover different areas, including data quality, study recruitment, adverse events, and the monitoring of specific hypotheses over time. Static and automated. Automated static reports are typically delivered once a week or at an interval of your choice, and delivered by email in a PDF format. [Read More]

Patient-centered communication

SporeData services

We have implemented several protocols to ensure that our designs are patient-centered. Below is a brief description: Contact and active participation throughout the study lifecycle. We create designs where patients and other stakeholders can be active participants throughout the study. These designs follow [PCORI methods recommendations]](https://www.pcori.org/research-results/about-our-research/research-methodology/pcori-methodology-standards). These activities start with joint research design sessions, where the study design is shared and discussed with patients and other stakeholders, such as healthcare professionals, hospital administrators, policymakers, government officials, among others. [Read More]