Deep learning for mammography: Improving accuracy and productivity in breast cancer diagnosis.

Informazioni relative al progetto

MAMMO1

ID dell’accordo di sovvenzione: 837667

DOI

10.3030/837667

Progetto chiuso

Data della firma CE 21 Dicembre 2018

Data di avvio 1 Gennaio 2019

Data di completamento 30 Aprile 2019

Finanziato da

• INDUSTRIAL LEADERSHIP - Innovation In SMEs

Costo totale

€ 71 429,00

Contributo UE

€ 50 000,00

Coordinato da

KHEIRON MEDICAL TECHNOLOGIES LTD
United Kingdom

Periodic Reporting for period 1 - MAMMO1 (Deep learning for mammography: Improving accuracy and productivity in breast cancer diagnosis.)

Periodo di rendicontazione: 2019-01-01 al 2019-04-30

Sintesi del contesto e degli obiettivi generali del progetto

Mia (previously Mammo1) is deep-learning-based software designed to help radiologists and breast screening programmes. It uses convolutional neural networks to scan Full Field Digital Mammograms (FFDM), to identify patterns indicating cancer in a case-wise manner, offering a recall suggestion and highlighting the areas on which the decision is based. It can serve as a second or third reader, in triaging (making priority lists by detecting the most potentially malignant cases) and as a quality assessment tool.
The Phase I H2020 SME Instrument helped us to assess technical, commercial and financial aspects to ensure market uptake; study IP protection, research our freedom-to-operate; and identify potential risks we may encounter during development, commercialisation and system scale-up, and design mitigation measures.

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

Mia’s prototype has already been tested in a retrospective clinical study, validated externally. This demonstrated sensitivity and specificity of 90% and 89% respectively. These results surpass those of the best known competitors and led to successful awarding of the CE Mark class IIa for Medical Devices, and winning ‘Best New Radiology Software’ as awarded by the radiology community Aunt Minnie.
Mia can achieve robust generalisability across all major mammography hardware vendors and global screening settings.
We have investigated the requirements to integrate our software into hospital workflows and designed two deployment architectures, on-site and cloud-based solutions, to cover all options while ensuring security requirements are met.
To assess Mia’s generalisability, integration and accuracy we aim to perform prospective clinical trials. Several European hospitals are already interested in participating.

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)

Mia’s performance surpasses previous Computer-Assisted Diagnosis (CAD) solutions and the average single-reading radiologist. Designed to act as a second reader in blinded double-read settings with arbitration, reduces up to 50% in workload and manpower costs, while maintaining the low recall rates expected in population-based screening programmes. Additionally, Mia can help prioritise potentially malignant cases prior to review, improving speed of diagnosis. The wider implication is that nation-wide screening programmes suffering from the current breast screening workforce crisis will have the opportunity to provide high quality, fast and accurate screening for any woman, anywhere, with reduced geographic variability in outcomes.

How Mia Works

Hospital deployment architectures for Mia

Mia's performance compared to competitors and single radiologists