New solution for fully automated analysis and reporting of routine musculoskeletal X-rays

Informazioni relative al progetto

AutoRay

ID dell’accordo di sovvenzione: 855101

DOI

10.3030/855101

Progetto chiuso

Data della firma CE 18 Febbraio 2019

Data di avvio 1 Marzo 2019

Data di completamento 31 Maggio 2019

Finanziato da

• INDUSTRIAL LEADERSHIP - Innovation In SMEs

Costo totale

€ 71 429,00

Contributo UE

€ 50 000,00

Coordinato da

RADIOBOTICS APS
Denmark

Periodic Reporting for period 1 - AutoRay (New solution for fully automated analysis and reporting of routine musculoskeletal X-rays)

Periodo di rendicontazione: 2019-03-01 al 2019-05-31

Sintesi del contesto e degli obiettivi generali del progetto

Clinical imaging is essential to support diagnosis and therapeutic decision-making. Plain x-rays remain the main imaging technique for routine and emergency examinations. The length of time to analyze and report, and the potential risk to patients on receiving delayed treatments is a matter of concern in multiple countries. The flux of x-rays collected is superior to the capacity of the radiologists to analyze and prepare the medical reports in a timely manner, creating large backlogs.
Currently, the number of radiologists is insufficient to fill the demand. According to a UK workforce census 2017 report from the Royal College of Radiology, the diagnostic reporting workload has increased 30% in the last 5 years and that there is a shortfall of more than 1000 radiology consultants (open positions)
The objectives of the project are to develop a novel solution to automatically analyze and generate reports describing medical images. The software uses machine learning, combined with advanced image processing (computer vision) to accurately analyze x-ray images of the msk system in a standardized manner.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

We have interviewed 12 radiologists and medical doctors in 4 countries to document the requirements and technical needs to accept a solution to generate reports describing medical images of msk x-ray. We have quantified the addressable market and developed a business model. On the regulatory side, we have investigated certification requirements.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

We have developed a prototype of automatic report where clinical findings are used to generate automatic report describing clinical findings and impressions. Uses in two hospitals have tested it and gave feedback on its performance in term of feature detection and reporting accuracy. The software has been integrated in in open source APCS server, which has enabled us to perform integration tests and usability in-house.
An automatic report will enable patients to receive diagnosis, and thus potentially treatment, from their treating physician much faster.

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