Moletest (Scotland) Ltd, Edinburgh, United Kingdom.
Published: December 2024
AI Article Synopsis
Article Abstract
Four versions of the novel Moletest on-device signal-processing analysis software, delivered on a single-application iPadPro11, were evaluated in pigmented skin lesions of patients sent for biopsy comparing its results with the lesion histology. The primary objective was to demonstrate that the nomela test provided at least 95 ± 2% sensitivity for not-melanoma. Proprietary software recognised the lesion perimeter, rejecting images with uncertain border, and performed analysis using non-scalar metrics of the image within the perimeter. From five algorithms any one reading outside the predefined melanoma range assigned the lesion to 'no evidence of melanoma' or otherwise to 'melanoma not excluded'. 1365 participants provided 1573 lesions. 1389 valid cases had a valid signal-processing test result and a valid histology report defined as 'not-melanoma' ( = 1205) or 'melanoma' ( = 184). Statistical analysis for the four versions were: sensitivity (ability of the signal-processing analysis to assign 'No evidence of melanoma' to a lesion with 'not-melanoma' histology) 8.6/48.4/76.9/52.1%; specificity (ability to assign 'Melanoma not excluded' to a lesion with 'melanoma' histology) 100/79.6/75.0/65.2%. The performance of the Moletest signal-processing analysis as a diagnostic support for rule-out of melanoma showed limited value in all four versions.
As one of the most common neurodegenerative diseases, Multiple sclerosis (MS) is a chronic immune-driven disorder that affects the central nervous system (CNS). Due to the variety of symptoms, accurately diagnosing MS demands rigorous attention to differential diagnosis, as various disorders can closely mimic its clinical and paraclinical features. Although MR imaging techniques are gold standards in diagnosing MS, the feasibility of advanced Electroencephalogram (EEG) signal processing methods is discussed in this study to detect patients with MS disorder.
Four versions of the novel Moletest on-device signal-processing analysis software, delivered on a single-application iPadPro11, were evaluated in pigmented skin lesions of patients sent for biopsy comparing its results with the lesion histology. The primary objective was to demonstrate that the nomela test provided at least 95 ± 2% sensitivity for not-melanoma. Proprietary software recognised the lesion perimeter, rejecting images with uncertain border, and performed analysis using non-scalar metrics of the image within the perimeter.
School of Public Health, the key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 561113, China.
With the global prevalence of the hand-foot-and-mouth disease (HFMD) epidemic, the development of reliable point-of-care testing (POCT) is crucial for the timely identification and prevention of outbreaks. Isothermal nucleic acid amplification techniques (INAATs) have attracted much attention because of their high efficiency for rapid diagnosis. In this work, we systematically summarize the current status of INAATs for HFMD and discuss advantages and drawbacks of various INAATs for HFMD.
Objectives: This narrative review aims to analyze mechanisms underlying Brain-Computer Interface (BCI) and Artificial Intelligence (AI) integration, evaluate recent advances in signal acquisition and processing techniques, and assess AI-enhanced neural decoding strategies. The review identifies critical research gaps and examines emerging solutions across multiple domains of BCI-AI integration.
Methods: A narrative review was conducted using major biomedical and scientific databases including PubMed, Web of Science, IEEE Xplore, and Scopus (2014-2024).
State Key Laboratory of Green Pesticide, International Joint Research Center for Intelligent Biosensing Technology and Health, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China.
The complex composition of real plastic wastes poses a significant challenge for their large-scale disposal. A responsive on-site compositional analysis of plastics is informative in choosing downstream processing methods. Nanocatalyst-based assay kit is highly qualified for this scene; however, there remain no efficient nanocatalysts for plastics due to their highly inert chemistry.
