#artificialintelligence

Kunstig Intelligens & Sundhed

Kunstig Intelligens & Sundhed

Kunstig intelligens kommer til at forandre vores sundhedsvæsen. Det er en af de mest disruptive kræfter inden for sundhedssektoren hvad angår kvalitetsforbedring, og det baner vej for mange nye muligheder for innovative behandlinger. Derudover kan kunstig intelligens understøtte beslutninger og muliggøre hurtigere diagnoser samt behandling såvel som mere effektive arbejdsprocesser.

Projekter i fokus

ROPCA Robotten
ROPCA Ultrasound

En automatiseret medicinsk ultralydsundersøgelses- og analyserobot, som skal anvendes til diagnostik og evaluering af leddegigt

Billede af en kapsel som anvendes til trådløs endoskopi
Trådløs kapselendoskopi

En smart og sikker kamerapille, der muliggør patientvenlig inspektion af marvetarmkanalen

 I boksen nedenunder kan du læse mere om vores andre projekter, og skulle du ønske mere information, kan du tage kontakt til vores institutleder, Kasper Hallenborg. (OBS: Projekterne kan indtil videre kun læses på engelsk)

Kunstig Intelligens & Sundhed projekter

AI Supported Epilepsy Diagnosis

The efficiency of pharmaceutical therapy in patients with generic generalized epilepsy can be determined by monitoring, i.e. with EEG. The project pursues the development of video-based analysis, thereby providing feasible, non-invasive and comfortable long-term monitoring to determine treatment response.

Klik her for at læse mere om AI Supported Epilepsy Diagnosis.

AIA
AI for Audiology is a project with the Department of Audiology at OUH. In this project we use time series analysis using LSTM networks for classification of hearing tests.
AIID
AI for medical Image Diagnostics for diabetes patients is a collaboration between OUH Department of Ophthalmology and SDU Robotics, where AI based classification of retina images is used.
AISWE
AI on Shear Wave Elastography for liver patients is a project on developing automatic methods for estimating measures from ultrasound images. This project is a collaboration between FLASH Liver Research Center, OUH and SDU Robotics.
Capsule Robot

The purpose of the project is to run AI algorithm in the electronic chips inside the capsule robot, so that this robot can process the captured images locally and only transmit out the images with diseases.

Klik here for at læse mere om Capsule Robot.

CDP
Classification of Digital Pathology using AI is a project on automatic segmentation of pathological samples. This project is a collaboration between OUH Department of Pathology and SDU Robotics.
Chrohns Disease

The aim of this study is to develop a deep learning algorithm based on convolutional neural networks (CNN) for autonomous detection of Crohn’s Disease in the small bowel and colon.

Klik her for at læse mere om Crohn's Disease.

Diabetes and Diabetic Complications

To develop and apply innovative image processing and AI methods on patients’ videos, to discover tiny changes in external body features and identify which ones can be used as novel early markers of diabetes mellitus and specific diabetic complications.

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Early diagnosis of cancer
Together with OUH and SUND, SDU Health Informatics & Technology is involved in a project that aims to develop a predictive model for early diagnosis of cancer.
Fatty Liver Disease

To develop and validate an AI-based clinical decision aid for use in primary health care to accurately predict advanced fibrosis at an early stage, in patients with fatty liver disease.

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HABITUS
Together with SUND, SDU Health Informatics & Technology is involved in a project that aims to develop tools that analyze human activity behavior data.
PAITEM
Together with SUND and FAM, SVS (Esbjerg), SDU Health Informatics & Technology is currently developing prognostic AI tools for emergency medicine.
PDWS
Together with SUND and FAM, OUH (Odense), SDU Health Informatics & Technology is developing tools and predictive models that aims to identify patients at risk.
 

Klik her for at læse mere om PDWS.

Polyp Detection from Colorectal Acquired Images

The aim of the project is to detect polyps as intestinal lesions from images and video streams based on digital image processing methodologies. The results will aid physicians grading them in a fast, accurate, and reliable manner, and prescribing patients with appropriate treatment.

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Preeclampsia

To develop a non-invasive, image-processing based method, able to measure small variations in facial vessels; and investigate its ability to early predict the onset of preeclampsia.

Klik here for at læse mere om Preeclampsia.

Relip

Together with OUH and SUND, SDU Health Informatics & Technology is currently developing a predictive model for detection of alcohol use disorder.

Klik her for at læse mere om Relip.

Risk Estimate for Cardiovascular Events

The study combines the coronary artery calcification score together with biomarkers and socioeconomic status to improve the prediction of cardiovascular events in asymptomatic subjects. The study supports clinical decision making and thus the treatment due to specific, individual prognoses.

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ROPCA Ultrasound

Robotic platform for clinical application with ultrasound for arthritis patients to evaluate the disease score in images. This project is a collaboration between Svendborg Hospital, OUH and SDU Robotics.

Klik her for at læse mere om ROPCA.

ROSOR

This project is a collaboration between the department of Gynecology at OUH, Aarhus University and SDU Robotics. It researches in surgical robotics for oncology patients where AI instruments are used for 3D reconstruction of the surgical motions.

Klik her for at læse mere om ROSOR.

SDU eHealth Platform
Together with SUND, SDU Health Informatics & Technology is developing a configurable platform for eHealth services and interventions based on the experiences from the ongoing ACQUIRE-ICD and eMindYourHeart projects.
Sentinel Algorithm

Together with OUH, Region of Southern Denmark, Svendborg Municipality and SUND, SDU Health Informatics & Technology has developed a predictive model for early detection of deterioration of fragile elderly living at home.

Klik her for at læse mere om Sentinel Algorithm.

Skin Lesion Classification and Melanoma Detection

The aim of this project is to build a fully automated melanoma recognition system, which will correctly categorize image samples of human skin lesions.

Klik her for at læse mere om Skin Lesion Classification and Melanoma Detection.

Wireless Capsule Endoscopy

Implementing a Smart and Secure Camera Pill Using an edge detection algorithm to detect the significant images to improve efficiency of RF data transmission to extend battery life.

Klik her for at læse mere om Wireless Capsule Endoscopy.

Hvis du ønsker at vide mere om Kunstig Intelligens & Sundhed ved Mærsk Instituttet, kan du kontakte vores institutleder ved at klikke her

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