AI Powered Medical Solutions
Image Processing
- Automated image analysis for accurate classification and diagnosis
- Development of Deep Learning Neural Networks for early detection of diseases
Early Detection of Mesothelioma Through Cellular Profiling
AI is transforming mesothelioma diagnosis by analysing malignant cell images for early detection. This rare cancer, often diagnosed late, can now be identified sooner through AI-driven cellular profiling, improving patient outcomes.
AI-Powered Cellular Profiling
AI deep learning models analyse histopathological images to detect malignant mesothelioma with high precision. Key advancements include:
- Automated Image Analysis – AI scans biopsy slides to identify early cellular abnormalities.
- Feature Extraction & Pattern Recognition – AI detects distinct mesothelioma traits in cell morphology and tissue structure.
- Biomarker Identification – AI recognises genetic markers linked to mesothelioma for more accurate diagnosis.
Enhancing Diagnostic Accuracy with AI
AI models, especially convolutional neural networks (CNNs), classify mesothelioma subtypes with high sensitivity. Integration with liquid biopsy data further refines accuracy, supporting:
- Tumour Classification – AI distinguishes mesothelioma types for tailored treatment plans.
- Predictive Analytics – AI forecasts treatment responses, enabling personalised therapy.
By leveraging AI for mesothelioma detection, medical professionals can diagnose earlier, improve treatment strategies, and extend patient survival.
Segmentation of Mucus Goblet Cells in Microscopic Images
AI is revolutionising the segmentation of mucus goblet cells in microscopic images, enhancing the study of respiratory and gastrointestinal diseases. These cells, which produce mucus to protect epithelial linings, play a key role in conditions like asthma, chronic obstructive pulmonary disease (COPD), and inflammatory bowel disease (IBD).
AI-Powered Goblet Cell Segmentation
Deep learning models, particularly convolutional neural networks (CNNs), improve the accuracy of goblet cell identification in histopathological images. Key advancements include:
- Automated Cell Detection – AI distinguishes goblet cells from surrounding epithelial structures with high precision.
- Morphological Analysis – AI assesses cell size, shape, and density to detect abnormalities linked to disease.
- Quantitative Insights – AI provides cell counts and mucus secretion levels for disease progression analysis.
Enhancing Research and Diagnosis
AI segmentation enables more precise histopathological assessments, aiding in:
- Early Disease Detection – AI flags abnormal mucus production patterns for faster diagnosis.
- Treatment Monitoring – AI tracks changes in goblet cell populations to assess therapy effectiveness.
Future Applications
AI-driven segmentation is shaping new approaches in clinical research, including:
- Real-Time Pathology Analysis – AI-integrated imaging tools for instant diagnostics.
- Personalised Medicine – AI-driven insights into mucus-related disorders for tailored treatments.
Researchers and clinicians can enhance diagnostics, monitor treatments, and advance personalised healthcare solutions by applying AI to goblet cell segmentation.
Red = sarcomatoid (bad)
Blue = epitheliod (good)
Data Processing and Optimisation
Harnessing AI to streamline and enhance medical data analysis, our advanced solutions ensure faster, more accurate insights for patient risk assessment and treatment evaluation.
Automated Patient Data Parsing
Our AI-driven systems automatically process vast volumes of patient data, identifying key health indicators to support early intervention and precision medicine.
Small Dedicated Neural Networks
We develop tailored neural networks designed to detect high-risk patients, improving early diagnosis and personalised treatment strategies.
Genetic Algorithms for Medical Innovation
Our bespoke Genetic Algorithms (GA) evaluate new treatments and drugs, optimising therapeutic approaches by simulating evolutionary processes to identify the most effective solutions.
Real Applications
- Early Detection of High-Risk Patients – AI models predict severe responses to conditions like COVID-19.
- Suicide Risk Profiling – Identifies at-risk children and vulnerable populations, supporting early intervention.
- Disease Threat Categorisation – AI-driven segmentation assesses population health risks and treatment success probabilities.
- Drug Effectiveness Quantification – Genetic Algorithms optimise drug evaluation, enhancing treatment outcomes.
By integrating AI-driven data processing and optimisation, we enable healthcare providers to make data-backed decisions, improving patient outcomes and advancing medical research
Propietary Technologies
Neural Networks
Small and agile networks suitable for:
- Classification
- Surrogate Development
- Forecasting
- Pattern Recognition
- Image Processing
Image Processing
Larger Convolutional networks suitable for:
- Image Classification
- Image Segmentation
- Background Removal
- Genetic Algorithms
Genetic Algorithms
Hybrid evolutionary optimisation tools using multiple cross-over / mutation / evolution techniques suitable for:
- Global Multi-Parameter Multi-Objective Optimisation
Publications
- Marc, S.T., Belavkin, R., Windridge, D. and Gao, X., 2023. An Evolutionary Approach to Automated Class-Specific Data Augmentation for Image Classification. In International Conference on the Dynamics of Information Systems. Cham: Springer Nature Switzerland.
- Eastwood, M., Sailem, H., Marc, S.T., Gao, X., Offman, J., Karteris, E., Fernandez, A.M., Jonigk, D., Cookson, W., Moffatt, M. and Popat, S., 2023. MesoGraph: Automatic profiling of mesothelioma subtypes from histological images. Cell Reports Medicine.
Custom AI Services
