The Holistic Perspective of the INCISIVE Project—Artificial Intelligence in Screening Mammography

Lazic, Ivan; Agullo, Ferran; Aussó, Susanna; Alves, Bruno José Rodrigues; Barelle, Caroline; Berral, Josep Ll.; Bizopoulos, Paschalis; Bunduc, Oana; Chouvarda, Ioanna; Dominguez, Didier; Filos, Dimitrios; Gutierrez-Torre, Alberto; Hesso, Iman; Jakovljević, Nikša; Kayyali, Reem; Kogut-Czarkowska, Magdalena; Kosvyra, Alexandra; Lalas, Antonios; Lavdaniti, Maria; Lončar-Turukalo, Tatjana; Martinez-Alabart, Sara; Michas, Nassos; Nabhani‐Gebara, Shereen; Raptopoulos, Andreas; Roussakis, Yiannis; Stalika, Evangelia; Symvoulidis, Chrysostomos; Tsave, Olga; Votis, Konstantinos; Charalambous, Andreas

doi:10.3390/app12178755

Cited by 7 publications

(2 citation statements)

References 47 publications

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“…In this direction, five EU projects (Primage [ 4 ], CHAIMELEON [ 5 ], ProCΑncer-I [ 6 ], INCISIVE [ 7 ], and EuCanImage [ 8 ]) are working together under the AI for health imaging (AI4HI) initiative, sharing experience and good-practices towards the development of big data infrastructures based on European, ethical and General Data Protection Regulation (GDPR) compliant, quality-controlled, cancer-related, medical imaging, and related patient’s data platforms, in which both large-scale data and AI algorithms will co-exist. The overarching vision of the platforms developed by these projects can be further specialized and made more concrete as follows: Compliant with FAIR data principles: the platforms should be built on FAIR-compliant and secure design and support data governance that enables sustainable cross-border connection of pan-cancer image data sources Built for all involved stakeholders: the designed systems should be primarily aimed for use by clinicians, researchers, AI modellers, and innovators.…”

Section: Introductionmentioning

confidence: 99%

Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects

et al. 2023

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Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of ‘sick-care’ to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population, and disease diversity. However, to date, efforts are fragmented, based on single–institution, size-limited, and annotation-limited datasets. Available public datasets (e.g., The Cancer Imaging Archive, TCIA, USA) are limited in scope, making model generalizability really difficult. In this direction, five European Union projects are currently working on the development of big data infrastructures that will enable European, ethically and General Data Protection Regulation-compliant, quality-controlled, cancer-related, medical imaging platforms, in which both large-scale data and AI algorithms will coexist. The vision is to create sustainable AI cloud-based platforms for the development, implementation, verification, and validation of trustable, usable, and reliable AI models for addressing specific unmet needs regarding cancer care provision. In this paper, we present an overview of the development efforts highlighting challenges and approaches selected providing valuable feedback to future attempts in the area.Key points• Artificial intelligence models for health imaging require access to large amounts of harmonized imaging data and metadata.• Main infrastructures adopted either collect centrally anonymized data or enable access to pseudonymized distributed data.• Developing a common data model for storing all relevant information is a challenge.• Trust of data providers in data sharing initiatives is essential.• An online European Union meta-tool-repository is a necessity minimizing effort duplication for the various projects in the area.

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Section: Introductionmentioning

confidence: 99%

Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects

et al. 2023

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“…INCISIVE is a European Union (EU) Horizon 2020 funded project that brings together 26 industrial, clinical, and academic partners from across 9 European countries [ 21 ]. The project has two main aims.…”

Section: Introductionmentioning

confidence: 99%

Cancer care at the time of the fourth industrial revolution: an insight to healthcare professionals’ perspectives on cancer care and artificial intelligence

Hesso,

Kayyali,

Dolton

et al. 2023

Radiat Oncol

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Background The integration of Artificial Intelligence (AI) technology in cancer care has gained unprecedented global attention over the past few decades. This has impacted the way that cancer care is practiced and delivered across settings. The purpose of this study was to explore the perspectives and experiences of healthcare professionals (HCPs) on cancer treatment and the need for AI. This study is a part of the INCISIVE European Union H2020 project's development of user requirements, which aims to fully explore the potential of AI-based cancer imaging technologies. Methods A mixed-methods research design was employed. HCPs participating in cancer care in the UK, Greece, Italy, Spain, Cyprus, and Serbia were first surveyed anonymously online. Twenty-seven HCPs then participated in semi-structured interviews. Appropriate statistical method was adopted to report the survey results by using SPSS. The interviews were audio recorded, verbatim transcribed, and then thematically analysed supported by NVIVO. Results The survey drew responses from 95 HCPs. The occurrence of diagnostic delay was reported by 56% (n = 28/50) for breast cancer, 64% (n = 27/42) for lung cancer, 76% (n = 34/45) for colorectal cancer and 42% (n = 16/38) for prostate cancer. A proportion of participants reported the occurrence of false positives in the accuracy of the current imaging techniques used: 64% (n = 32/50) reported this for breast cancer, 60% (n = 25/42) for lung cancer, 51% (n = 23/45) for colorectal cancer and 45% (n = 17/38) for prostate cancer. All participants agreed that the use of technology would enhance the care pathway for cancer patients. Despite the positive perspectives toward AI, certain limitations were also recorded. The majority (73%) of respondents (n = 69/95) reported they had never utilised technology in the care pathway which necessitates the need for education and training in the qualitative finding; compared to 27% (n = 26/95) who had and were still using it. Most, 89% of respondents (n = 85/95) said they would be opened to providing AI-based services in the future to improve medical imaging for cancer care. Interviews with HCPs revealed lack of widespread preparedness for AI in oncology, several barriers to introducing AI, and a need for education and training. Provision of AI training, increasing public awareness of AI, using evidence-based technology, and developing AI based interventions that will not replace HCPs were some of the recommendations. Conclusion HCPs reported favourable opinions of AI-based cancer imaging technologies and noted a number of care pathway concerns where AI can be useful. For the future design and execution of the INCISIVE project and other comparable AI-based projects, the characteristics and recommendations offered in the current research can serve as a reference.

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Evaluation of Deep Learning Techniques for Automatic Lesion Segmentation in Mammography Images

Lazic,

Jakovljevic,

Rapaic

et al. 2024

IFMBE Proceedings

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The Holistic Perspective of the INCISIVE Project—Artificial Intelligence in Screening Mammography

Cited by 7 publications

References 47 publications

Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects

Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects

Cancer care at the time of the fourth industrial revolution: an insight to healthcare professionals’ perspectives on cancer care and artificial intelligence

Evaluation of Deep Learning Techniques for Automatic Lesion Segmentation in Mammography Images

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