Lung Abnormality Prediction using AI and Human Machine Interaction in CT images/ Radiological Structured Reporting

IQBMI holds the second webinar to enhance the artificial intelligent role from the data to the diagnosis for Lung pathologies.

Using AI in differential diagnosis is an exciting phenomenon which is getting so popular in all over the world; Artificial intelligence (AI) has the potential to increase the efficiency of lung cancer screening.

Lung cancer is the leading cause of cancer-related death worldwide, for which the 5-year survival rates have yet to surpass 20% .

There are a variety of pathologies in respiratory system such as pneumonia, COVID_19, SARS, atelectasis, nodules, pleural effusion, lymphatic pathologies, asthma and other types of lung disorders. Chest computer tomography (CT) is being used extensively in patients with known and suspected infection, especially when there is limited availability of conventional tests.

In this webinar, Speakers will discuss about Lung Abnormality Prediction using AI, Interaction between Human and AI in Medicine, and the role of Radiological Structured Reporting .

Topics by Presenters

1. ” Lung Abnormality Prediction using AI”

2. “Improving the Interaction between Human and AI in Medicine”

3- “Radiological Structured Reporting for Lung pathologies”

Objectives

 The role of AI in Prediction of Abnormalities

 How AI can help Radiologists

 Presentation of the different AI-based solutions at UMCG including : lung nodule / emphysema / Covid19 / pneumothorax

 Accuracy of diagnosis using AI as an assistance tool

 Improving the interaction of Human and AI

 Enhancement of using Structured Reporting in radiological images designed at IQBMI

Peter Van Ooijen

Coordinator Machine Learning Lab at Data Science Center in Health (DASH), UMCG

Working in Radiology research both on image processing and PACS

1) Installation and coordination of digitalization of the radiology department and the transition to a completely filmless hospital
2) Research on image processing in radiology
3) Participation in multiple research projects in different medical fields (including cardiology, orthopedics, pulmonology, etc)
4) Supervision of MSc and PhD students

P van Ooijen is (Co)Author of over 70international, peer reviewed, publications (enlisted in PubMed) and (co)author of numerous other publications, book chapters and conference abstracts. This results in an h-factor of 14 according to the web of science ranking.

Additional functions:

* Member of the editorial advisory committee of Hospital IT Europe, Campden Publishing
* Subcommittee Member European Congress of Radiology (ECR) 2008 and 2009 of the subcommittee on cardiac radiology
* Member of the Self-Assessment subcommittee of the European Society of Radiology
* Member of the BVT (Imaging Techniques) committee of the NVvR (Dutch society of radiology) for the development of technical courses for residents.

Specialties: PACS implementation, Image processing, DICOM, clinical imaging, radiology.

Mojtaba Barzegar

CEO & co-founder of iqbmi , IQBMI

he has capabilities on below fields :
_ Management the Imaging department
_ Archiving the images and signals in the bio_bank storage
_ Collaboration as a referee for related fields
_ Radiotherapy Planning
_ Professor assistant in related fields
_ Working as MRI professional technologist with over 10 years of experience
_ Developing Quantitative Structured Reporting at La Fe Hospital, Valencia, Spain

Rosa Verhoeven

Honours MSc student Human-Machine Communication

Highly motivated master student Human-Machine Communication, striving towards a PhD!

The study in Human-Machine Communication builds upon my bachelor in Artificial Intelligence, while taking on a more human cognition-focused perspective. With this background, I aim to pursue a career which allows me to combine two of my greatest interests: Artificial Intelligence and Healthcare. By performing research and developing innovative products, I want to contribute to the improvement of the medical world.

The Role of AI in Differential Diagnosis of Lung Pathologies

The Role of AI in Differential Diagnosis of Lung Pathologies

Presenters:

27th:

Raym Geis, MD, Vice Chair, Informatics Commission of ACR (U.S.A)

Shoma Sharma, Radiologist , Researcher at CARING ( India)

Jose Sánchez García, Data Scientist at QUIBIM ( Spain)

Suthirth Vaidya, Biomedical Engineering, Co-Founder at Predible Health (India)

28th:

Guy Frija, Radiologist, Eurosafe imaging chairman (France)

Dariush Eftekharpour, Radiologist, Researcher at IQBMI (I.R.Iran )

Robert Kitlowski, Researcher, CEO at BrainScan.AI (Poland)

Mojtaba Barzegar, Medical Physicist, Researcher & CEO at IQBMI (I.R.Iran)

Date of Webinar: 27-28 August 2020, 16:30_17:30 GMT

worldwide time zone

About the webinar

IQBMI holds this webinar to enhance the artificial intelligent role from the data to the diagnosis.

Using AI in differential diagnosis is an exciting phenomenon which is getting so popular in all over the world; although there are some challenges in usage of this novel technology in modern imaging diagnosis as an algorithm to find more specific features.

There are a variety of pathologies in respiratory system such as pneumonia, COVID_19, SARS, atelectasis, nodules, pleural effusion, lymphatic pathologies, asthma and other types of lung disorders. Chest computer tomography (CT) is being used extensively in patients with known and suspected infection, especially when there is limited availability of conventional tests.

In this webinar, radiologists will discuss with AI experts and speak about available challenges to be solved with this phenomenon while radiologists work with.

Learning objectives

1. To learn about AI usage in differential diagnosis of CTs.

2. To understand issues with COVID-19 infection in Chest CTs.

About the topics

 Pitfalls and misdiagnostic findings in the pandemic infectious era

 Different findings in chest CT scan

 Safety and Quality with use of AI

 The role of AI in celerity and accuracy of reports

 Challenges in making a database as an index for diagnosis

 Some additional benefits from using machine learning in diagnosis

 Dose reduction using AI

 Discussion panel

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