Jazz

AI Medical

Jazz is software for the digitalization of radiological findings, with quantitative single lesion tracking. Jazz is aimed at improving the assessment of the evolution of complex brain pathologies such as chronic multiple sclerosis or multi-metastatic brain disease.

Information source: Vendor
Last updated: March 24, 2024

General Information

General
Product name	Jazz
Company	AI Medical
Subspeciality	Neuro
Modality	MR
Disease targeted	Multiple sclerosis (MS), brain metastasis
Key-features	Brain lesion detection, volume quantification
Suggested use	During: interactive decision support (shows abnormalities/results only on demand), report suggestion

Technical Specifications

Data characteristics
Population	Patients with known diagnosis of multiple sclerosis or brain metastasis
Input	Any brain MRI
Input format	DICOM
Output	Report with user prefered items such as evolution, anatomical position, mcDonald criteria (for MS), lesion treatment (for metastasis), etc
Output format	DICOM, pdf, rtf
Technology
Integration	Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration CIS (Clinical Information System)
Deployment	Locally on dedicated hardware
Trigger for analysis	Automatically, right after the image acquisition
Processing time	3 - 10 seconds

Regulatory

Certification
CE	Certified, Class IIa , MDR
FDA	510(k) cleared , Class II
Intended Use Statements
Intended use (according to CE)	Jazz is intended for the labeling, visualization and volumetric quantification of segmentable brain structures from a set of CT or MR images, and the production of a radiological report.

Market

Market presence
On market since	02-2023
Distribution channels
Countries present (clinical, non-research use)
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing
Pricing model	Subscription
Based on

Evidence

Evidence
Peer reviewed papers on performance	Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software (read)
Non-peer reviewed papers on performance	Improved Detection, Description and Efficiency of Multiple Sclerosis Neuroradiological Lesions’ Assessment using a Semi-Automatic Dedicated Deep-Learning Based Software Jazz (read)
Other relevant papers