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Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - Neurocloud (CLOUD-BASED MULTIMODAL NEUROIMAGING PLATFORM)

Teaser

Summary

Diagnostic accuracy is acknowledged as one of the main challenges both for physicians and healthcare systems. Medical literature points out that todayÂ´s human subjective interpretation of medical imaging can lead to a 20-30% miss rate (false negatives) and 2-15% false positive rate. During the period 2015-2017, Qubiotech evolved the algorithm and developed Neurocloud, a proprietary cloud-computing platform designed to host image analysis algorithms providing essential features such as DICOM images upload and manipulation, automatic report generation and medical image visualization.The main objective of the H2020-SME Phase I feasibility study was to assess whether both our new products development strategy and the commercial orientation of the new products were well targeted to take advantage of the identified market niche. The feasibility study has allowed us to set the strategic guidelines for driving the development of the company:

1. R+D and new products development:
Neurocloud should evolve to a safe and collaborative platform providing clinically relevant image markers for the diagnosis of neurodegenerative diseases that can interoperate along the clinical value chain. A prototype for enabling safe and interoperable connections to clinical systems has been tested. The knowledge gathered during the project has allowed us to define an updated R+D pipeline for the coming years.

2. Compliance with regulations:
A multicentre study for clinical validation shows limitations difficult to overcome in terms of results comparisons. Different practices in diseases management are beyond the scope of the validation, but will put a great bias on the results interpretation. The safest way to get a robust validation is by testing against databases of images, as large as possible. This can be only done with a retrospective validation procedure, that we will incorporate to our products development.

3. Commercial strategy:
The evaluation of the cost/benefit ratio of the use of Neurocloud in clinical practice is difficult because of the diverse casuistry of neurodegenerative diseases and the absence, in many cases, of curatorial therapies. Business feasibility can not depend on the fitting to reimbursement schemes, but on the value provided to physicians and hospitals in terms of savings and efficiency improvements. We should carry on quantification studies to support commercialization.

Overall, the feasibility study has allowed us to obtain information on three complementary aspects: clinical, technological and market. The impact of this information has been critical in the elaboration of an improved business plan focused on the development of products that scale in the clinical value chain and that are marketable on an international scale.

Work performed

1. Identify relevant clinical image markers for the diagnosis of neurodegenerative diseases. We have developed a new R+D pipeline and defined the products to be launched from 2019 to 2021
2. We have defined a clinical validation strategy to overcome regulatory barriers in Europe and USA for current and future products. We have also defined an IP strategy to protect future developments
3. We have surveyed reimbursement policies for molecular neuroimaging in five european countries. Lack of homogeneous criteria has been found, so we have put commercialization focus on providing value by imrpoving efficiency on the clinical value chain, rather on focusing on specific pathologies. We have also participated in several ehealth events for meeting with investors and other stake holders to gather business intelligence.
4. We have built a new business plan to raise a seed funding series and drive company growth for the next 3 years.

Final results

We have been able to define a detailed new products pipeline aimed at addresing current and future clinical needs in the field of neuroimaging diagnostics.Qubiotechâ€™s R&D pipeline for the period 2018-2021 is strongly focused at keep developing Neurocloud environment, moving forward from image quantification to deep learning diagnostic, incorporating new imaging biomarkers for Epilepsy, Alzheimerâ€™s, and ParkinsonÂ´s diseases. In addition, productivity
enhancement tools such as automatic reporting and PACS integration is being developed in parallel.
Socio-economic impact will come from increased introduction of image quantification into clinical workflow. It is expected to reduce the current rate of diagnostics errors and foster early detection of neurodegenerative diseases.