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Synergy-Net: Artificial Intelligence at the Service of Oncological Prevention

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Handbook of Artificial Intelligence in Healthcare

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 211))

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Abstract

In recent years the constant development of diagnostic techniques has contributed to improving the prognosis of many diseases. Among all, oncological diseases remain those in which a correct and early diagnosis can not only significantly improve the patient’s quality of life but also impact the effectiveness of the therapy itself. Artificial Intelligence can provide valuable aid to this need through the development of predictive models to support the physicians in the diagnosis of the disease. The project “Synergy-Net: Research and Digital Solutions in the Fight Against Oncological Diseases”, born from the collaboration between the Department of Medical and Advanced Surgical Sciences of the University of Campania “L. Vanvitelli”, the National Informatics Inter-University Consortium (National Informatics Inter-University Consortium), Lab ITEM “C. Savy” and Bollino IT S.p.A., aims at the realisation of a technological platform to support the early oncological diagnosis based on the integration of an interoperable communication and clinical data management system leveraging AI. The project has a deeply interdisciplinary nature (lung cancer, breast cancer, colorectal cancer, gastrointestinal carcinomas, prostate cancer, thyroid cancer and malignant skin tumours), which requires the collaboration of very different professionals, including general practitioners, specialist doctors, radiologists, surgeons, pathologists, molecular biologists and oncologists, as well as the support of a team of researchers for aspects related to machine learning and expert system development in health care. The core of the project consists in the creation of a Computer-Aided Detection/Diagnosis (Computer-Aided Detection/Diagnosis) system that, based on Machine Learning and Deep Learning techniques, assists the operator in the analysis of screening data such as anamnestic information, blood tests, instrumental and diagnostic images. The assistance to the operator is achieved by suggesting the portions of information (e.g. regions in an X-ray image) on which to focus more attention. The use of the system will help the physician in the development of increasingly personalised diagnostic and therapeutic strategies, meeting the criteria of tailored therapy/surgery, a desirable objective of any cancer prevention program.

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Notes

  1. 1.

    http://www.scope.unina.it.

  2. 2.

    https://luna16.grand-challenge.org/.

  3. 3.

    http://www.scope.unina.it.

Abbreviations

AI:

Artificial Intelligence

AUC-ROC:

Area Under the Receiver Operating Characteristic Curve

BCC:

Basal Cell Carcinoma

CAD:

Computer-Aided Detection/Diagnosis

CBIR:

Picture Archiving and Communication Systems

CINI:

National Informatics Inter-University Consortium

CLD:

Color Layout Descriptor

CNN:

Convolution Neural Network

CT:

Computed Tomography

CV:

Cross Validaion

D-CNN:

Deep Convolution Neural Network

DC:

Dominant Colour

DCE-MRI:

Dynamic Contrast Enhanced Magnetic Resonance Imaging

DICOM:

Digital Imaging and Communications in Medicine

DL:

Deep Learning

FN:

False Negative

FP:

False Positive

GI:

Gastrointestinal

HMMD:

Hue-Min-Max-Difference

HSV:

Hue-Saturation-Value

IoU:

Intersection over Union

k-NN:

k-Nearest Neighbors

MAP:

Mean Average Precision

ML:

Machine Leaning

MRI:

Magnetic Resonance Imaging.

NB:

Narrowband

NBI:

Narrow Band Imaging

NN:

Neural Network

P:

FPrecision

PACS:

Picture Archiving and Communication Systems

PET:

Positron Emission Tomography

R:

Recall

R-CNN:

Region CNN

ROC:

Area Under the Receiver Operating Characteristic Curve

ROI:

Regions of Interest

SCC:

Squamous Cell Carcinoma

SCD:

Scalable Color Descriptor

SEN:

Sensitivity

SPECT:

Single-Photon Emission Computed Tomography

SSD:

Single-Shot Detector

SVM:

Support-Vector Machine

TP:

True Positive

US:

UltraSound

UV:

Ultraviolet

WHO:

World Health Organization

WLR:

White Light Reflectance

YOLO:

Only-Look-Once

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Acknowledgements

This work is part of the “Synergy-net: Research and Digital Solutions against Cancer” project (funded in the framework of the POR Campania FESR 2014-2020—CUP B61C17000090007). We thanks professor G. Argenziano (director of Dermatology Unit), professor M. Santini (director of Thoracic Surgery Unit), professor E. Procaccini (director of Breast Unit), professor V. Napolitano (director of Endoscopy Unit), professor M. Romano (director of Gastroenterology Unit), professor M. De Sio (director of Urology Unit) and professor G. Docimo (director of Thyroid Surgery Unit) from Universitá della Campania “L.Vanvitelli” for providing data, giving insights and useful discussions. We also thanks Ernesto de Rosa (Bollino.IT) for the support and effort pushed to sustain the Synergy.Net project.

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Authors and Affiliations

  1. Bollino IT S.p.A., Via delle Industrie 31, 80147, Naples, Italy

    Ruggiero Bollino & Francesco Cipolletta

  2. DAMSS, Universitá della Campania “L.Vanvitelli”, P.zza L. Miraglia 2, 80138, Naples, Italy

    Ludovico Docimo & Domenico Parmeggiani

  3. University of Naples Federico II, Via Claudio 21, 80125, Naples, Italy

    Giampaolo Bovenzi, Michela Gravina, Stefano Marrone & Carlo Sansone

  4. CINI, Laboratorio ITEM “C.Savy”, Via Cintia 21, 80125, Naples, Italy

    Giampaolo Bovenzi & Carlo Sansone

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  1. Ruggiero Bollino
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Correspondence to Stefano Marrone .

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Editors and Affiliations

  1. Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, VIC, Australia

    Chee-Peng Lim

  2. Royal Adelaide Hospital, Adelaide, SA, Australia

    Ashlesha Vaidya

  3. St. Joseph’s Hospital and Medical Center, Stockton, CA, USA

    Kiran Jain

  4. Avanti Institute of Cardiology, Nagpur, Maharashtra, India

    Virag U. Mahorkar

  5. KES International, Shoreham-by-Sea, UK

    Lakhmi C. Jain

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Bollino, R. et al. (2022). Synergy-Net: Artificial Intelligence at the Service of Oncological Prevention. In: Lim, CP., Vaidya, A., Jain, K., Mahorkar, V.U., Jain, L.C. (eds) Handbook of Artificial Intelligence in Healthcare. Intelligent Systems Reference Library, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-030-79161-2_16

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