AI & Health

AI & Health

AI has the power to change the way healthcare is delivered. It is arguably one of the biggest disrupting forces in healthcare when it comes to quality improvement, and it provides many new opportunities for innovative treatments. Finally, AI can support decision making, and enable faster diagnostics and treatment as well as more efficient workflows.

Highlighted projects

The ROPCA Robot in action
ROPCA Ultrasound

An automatized medical ultrasound examination and interpretation robot which will ensure doctors the best possible basis for determining treatment

Capsule used for wireless endoscopy
Wireless Capsule Endoscopy

A smart and secure camera pill that allows for patient-friendly inspection of gastrointestinal tract

 Read more about our other exciting projects in the box below, and feel free to reach out to our director, Kasper Hallenborg, for more information.  

AI & Health projects

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.

Click here to learn more about 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.

Click here to learn more about 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.
Crohns 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.

Click here to learn more about 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.

Click here to learn more about Diabetes and Diabetic Complications.

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.

Click here to learn more about Fatty Liver Disease.

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.
 

Click here to learn more about 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.

Click here to learn more about Polyp Detection.

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.

Click here to learn more about Preeclampsia.

Relip

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

Click here to learn more about 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.

Click here to learn more about Risk Estimate for Cardiovascular Events.

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.

Click here to learn more about 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.

Read more about ROSOR here.

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.

Click here to learn more about 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.

Click here to learn more about 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.

Click here to learn more about Wireless Capsule Endoscopy.

 

For more information about AI & Health, press here to contact our director

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