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Better clinical outcomes through artificial intelligence-powered medical image analysis

Products

ChestGlass [explainer video] is a search engine that finds similar-looking radiographs in a given database.

The similarity is identified by a neural network, which judges both the pathology present as well as other features in the image (such as the location of the pathology, its severity etc.).

When implemented into a PACS/local system/cloud service ChestGlass empowers the user to quickly find retrospective cases with similar radiological appearance. This allows to study the report/final diagnosis/outcome of the patient.

Benefits:

  • workflow: facilitate the diagnostic process and improve accuracy
  • teaching: reduce the learning curve for radiology residents
  • research: find cases that stand out

ChestEye [explainer video] is a fully automatic computer-aided diagnosis (CAD) solution. It currently supports 75 radiological findings, and is able to localize these features on a radiogram in terms of a heatmap. ChestEye is under constant improvement with the goal to generate preliminary reports, that incorporate all the radiologically relevant information present in a chest X-ray radiograph.

The following radiological findings are supported: Linear Atelectasis, Lobar Collapse, Enlarged Heart, Edema, Pleural Effusion, Loculated Effusion, Fissural Thickening, Bullous Emphysema, Pulmonary Emphysema, Subcutaneous Emphysema, Consolidation, Pneumothorax, Tuberculosis, Hypoventilation, Lymphadenopathy, Hypertension, Granuloma, Lymph Node Calficiation, Elevated Diaphragm, Dislocated Mediastinum, Widened Mediastinum, Congestion, Fibrosis, Interstitial Markings, Pleural Adhesion, Hilar Prominence, Mass, Cyst, Pulmonary Cavity, Sarcoidosis, Hernia, Removed Lung, Enlarged Aorta, Goitre, Thymus, Aortic Sclerosis, Respiratory Distress Syndrome, Retrosternal Airspace Obliteration, Pleural Thickening, Pneumomediastinum, Pericardial Effusion, Pleural Plaque, Pneumoperitoneum, CV Catheter RA Placement, CV Catheter SVC Placement, HD Catheter RA Placement, HD Catheter SVC Placement, Catheter Malposition, Intubation, Intubation Malposition, Chest Tube, Sternal Wires, Endovascular Stent, Tracheael Stent, Esophageal Stent, Artificial Heart Valve, Intra Aortic Balloon, Ventricular Assist Device, Nasogastric Tube, Pacemaker, Spinal Implant, Azygos Lobe, Gastric Bubble, Bowel Gas, Barium Swallow, Abnormal Rib, Rib Resection, Spinal Compression Fracture, Spinal Degenerative Changes, Spondylosis, Osteoporosis, Kyphosis, Scoliosis, Ligament Ossification, Spinal Enthesopathy.

Integration with several PACS/DICOM viewers is under way. Cloud and local (non-cloud) deployment is also possible.

ChestEye is applicable to chest X-ray PA and AP images. LAT image support is in development.

Performance: area under curve (AUC) averaged over all the features is 0.91.

QEye is a radiological queue management/patient prioritization solution, which automatically prioritizes potentially unhealthy patients. Currently, queues of images awaiting description by the radiologist can be sorted by exam time, patient name, modality, the imaging site (device), and possibly a priority column, where the priority is currently set by a radiographer. In our proposed solution, the priority column would be provided by our QEye solution (see figure, indicated by a red rectangle).

Problem and Focus

Medical imaging (radiology) is a highly-trained-labour intensive profession. This results in

Our deep learning (also known as artificial intelligence, machine learning, artificial neural networks) expertise and data from our partners (hospitals and healthcare companies) allow us to alleviate these problems by reducing routine work and increasing diagnostic accuracy.

Team

Our cross-disciplinary team boasts several gold medals in various data science competitions, in addition to several decades of combined computer vision product development expertise. See below and the media section for more info.

Founders

Gediminas Pekšys , CEO

LinkedIn
  • BA of Mathematics, University of Cambridge
  • 5 years of experience in data science, computer vision and Deep Learning
  • 1st place in Deep Learning competition in Kaggle
  • Deep Learning paper review organizer

Jonas Bialopetravičius

LinkedIn
  • MSc in Computer Science, Vilnius University
  • PhD student in Astrophysics, focusing on Deep Learning applications
  • 7 years of experience in computer vision and machine learning in the field of biometrics
  • Two 1st places in Deep Learning competitions in Kaggle

Darius Barušauskas

LinkedIn
  • MSc in Econometrics, Vilnius University
  • 7 years of experience in machine learning and Deep Learning applications
  • Created over 30 models for financial, utilities & telco companies
  • International acknowledgement in online data science community
  • 5th overall rank in Kaggle, 5 prize finishes including three 1st places
  • Kaggle Grandmaster

Naglis Ramanauskas

LinkedIn
  • MSc in Medicine, Vilnius University
  • Resident doctor of Radiology, Vilnius University
  • Deep Learning experience in several radiology tasks
  • 1st place in 2017 AI Hackathon Vilnius
  • Founder of the Society of Innovative Medicine

Jogundas Armaitis

LinkedIn
  • PhD in Theoretical Physics, Utrecht University
  • 11 years of model building experience
  • Experience in applying Deep Learning to biometric and medical images
  • Marie Skłodowska-Curie Individual Fellow

Advisors

Ignas Namajūnas

LinkedIn
  • MSc in Computer Science, Vilnius University
  • Over 3 years of experience in computer vision industry and Deep Learning applications
  • Previously research lead for a Deep Learning based project
  • Two 1st places in Deep Learning competitions in Kaggle
  • 20th overall rank in Kaggle
  • Kaggle Grandmaster

Tomas Dirvonskas

LinkedIn
  • BA in Physics, MA in Communications and Economics, Vilnius University
  • IT Business Competence, Royal Institute of Technology, Stockholm
  • 12+ years business experience within mobile product area
  • Successfully sold mobile product development agency
  • Organizer of AI conference and hackathon AI Camp

Get in touch

Arrange a Skype call or a visit to our office in Vilnius, Lithuania

info@oxipit.ai