Summary description
1. Use
professional medical imaging and analysis test non-invasive diagnostic aid
indicates chance of melanoma in pigmented skin lesions (moles)
intended for use by authorised (trained) operators (medical professional) as adjunct screening technology in clinical pathway of the management of suspect lesions
2. On-device AI
supplied as proprietary software on a single-application iOS device
secure image capture with all other functions disabled
nomela® test and analysis by machine-learning AI made directly on the device
no requirement to internet connectivity for test and analysis completion
not an on-line web-based application
3. User management, Quality and IT security
authorised user management; site and date/time information, capture of minimal patient identification (NHS/CHI/hospital number acquired by barcode recognition in device), region & close-up images (for unequivocal identification of lesions), multiple lesion test capacity; e-consent included if required
no requirement for dermatoscopic images
QMS ISO13485 certified with regular audit by Notified Body (BSi); IT security certified by Cyber Security Plus and NHS DSP Toolkit; MDR Class IIa in process
4. Analysis technology
nomela® analysis subject to:
i) no contra-indications (operator confirms)
ii) distinct edge obtained (automated)
iii) size of lesion (not less than 5mm diameter including calculation by the test)analysis technology employs proprietary automated lesion edge detection and on-device machine-learning AI
5. Result and Reporting
only a few minutes required to complete the image capture, analysis and report
Result shown on the device screen as Low or High Chance of melanoma
Report shows user information, NHS/CHI/hospital number, high-quality images and nomela® analysis result(s)
available for local printing or upload by cellular (4G/5G) connection via AWS serverless server to secure (nhs.net) email or to allow access by authorised ePR
The nomela® AI architecture
The nomela® AI architecture (nomela® v6) is composed of two key components:
(i) a re-trained convolutional body model, adept at transforming input model images into a sequence of distinctive “features”, and
(ii) a fully connected head model responsible for making the ultimate classification decision. The training process exclusively focuses on fine-tuning the head model. More than 40 candidate body models were evaluated and compared to optimise performance.
Data description
The nomela® v6 AI system has been trained and validated rigorously on a data subset comprising 679 lesions (107 melanoma, 572 not-melanoma). The system has been subsequently tested on an independent subset of 450 lesions (70 melanoma, 380 not-melanoma). All lesion diagnoses had been confirmed by histopathology. For pre-processing, an automated technique has been, and is, used to delineate the lesions in a deterministic manner.
Results
The performance of the nomela® v6 AI system is equivalent to (dermatologist) experts assessing the same testing subset of images.