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Early Cancer Detection

The Process

Abnormal Cell Growth

A cancer tumor is a mass or growth of abnormal cells.

Target Patient

People of all ages, ethnicities, and social classes can develop cancerous tumors.

Benign or Malignant

Determining whether a tumor is benign or malignant is critical, as it directly impacts diagnosis and treatment decisions.

Tumor Type Classification

Malignant tumors have different shapes, sizes and impacts. Classifying the tumor correctly is important to plan treatment

 

Timely Diagnosis

Malignant tumors are dangerous and can spread quickly. Timely classification and treatment can save lives

 

Solution

An intelligent system designed to assist radiologists and other medical professionals in the accurate and timely detection, classification, and labeling of cancer images and mutations.

Model

A model is developed using state-of-the-art machine learning algorithms.

Training

The model is trained on large datasets of tumor images with multiple labels, leveraging deep learning capabilities.

Deployment

The model is deployed in the cloud for universal accessibility and is continuously improved over time.

Product

A COMPLETE SOLUTION FOR multiple types of cancer tumor classification using pathological images THAT CONTINUOUSLY LEARNS AND IMPROVES

 

Digital Image Input

Requires only a pathological image as input.

AI Examination

Cloud-based AI software processes the image and extracts tumor features.

Automated Classification

The AI system classifies tumors using advanced deep learning algorithms.

Universal Access

The solution is accessible globally through the internet

 

Data Platform

Data is central to our platform and plays a critical role in powering our AI engine and improving outcomes.

Our data science and engineering teams collaborate with leading research, commercial, and government data platforms to collect large-scale datasets that fuel our machine learning frameworks and AI algorithms. Our robust models and testing systems ensure high accuracy and reliability, allowing us to deploy the most effective features for each use case.

Our processes are fully compliant with global data privacy regulations.

Data Sources Include:

  • Major research universities

  • Government and non-government health organizations (CDC, NIH, NHS, etc.)

  • Public research data repositories (Figshare, Kaggle, etc.)

  • Commercial health and biometric data sources

Technology Platform

Our technology platform consists of the most scalable, robust and advanced global infrastructure and network combined with highly skilled data science and software development capabilities

Our platform is built on scalable, robust, and advanced global infrastructure combined with highly skilled data science and software engineering expertise.

  • Microsoft Azure Services Partner

  • Operates on Microsoft Azure for big data, machine learning, and AI capabilities

  • Collaborates directly with Microsoft Azure engineers to ensure high-quality software and services

Our Team
Our data science and software engineering teams include PhDs and master’s degree holders from leading American and international universities.

Methodology

Our methodology utilizes an enhanced convolutional neural network (CNN) model that achieves up to 93% accuracy across thousands of tumor classes.

Integrated radiomics

Model Architecture
Integrated radiomics combines X-rays, MRI, CT scans, and other medical imaging data. These inputs are processed through convolutional layers to enable advanced AI-driven analysis, tumor classification, and early cancer detection.

  • Small receptive field filters are used for linear and nonlinear transformations of input channels

  • Tailored fully connected layers classify tumors into categories such as meningioma, glioma, and pituitary tumors

  • The model is refined using Gabor filters and color blob detection

Datasets
Multiple tumor image datasets include thousands of T1-weighted contrast-enhanced images. These datasets are used to train and test the model.

Primary features analyzed include:

  • Tumor size

  • Position

  • Shape

  • Texture

Datasets are continuously updated and improved. 

 

Integrated Multiomics

AI-Driven Biomarker Discovery Using Multiomics Data

Doctari leverages integrated multiomics analysis to identify critical biomarkers for early cancer detection, risk prediction, and personalized diagnosis.

By combining multiple biological data layers—genomics, transcriptomics, metabolomics, and proteomics—our AI provides a deeper understanding of disease development at the molecular level.

 

Genome Sequencing

One of the indicators of certain types of cancer is the presence and analysis of bone marrow mononuclear cells.

Additional insights can be gained through genome mapping, where both germline and somatic mutations are analyzed to assess cancer risk and disease progression.

Deep learning plays a critical role in clinical oncology by supporting:

  • Early diagnosis

  • Prognosis prediction

  • Identification of cancers of unknown origin

  • Molecular subtyping of cancers

  • Precision oncology based on genomic data

This enables more accurate, data-driven decisions in cancer detection, treatment, and long-term patient outcomes.

Transcriptome Sequencing

The genome is not the only place where mutations can occur. The transcriptome—particularly RNA—can also influence the development of conditions such as MDS and AML. This area is less explored than genomics, and AI has the potential to drive new discoveries.

Transcriptomics-based machine learning can:

  • Predict AML status without requiring expert input

  • Support diagnosis and subclassification

When combined with machine learning as part of an integrated multiomics approach:

  • Genomics enables risk prediction, differential diagnosis, and subclassification

  • Transcriptomics supports diagnosis through AI-driven analysis

Metabolomics

Metabolomics focuses on small-molecule metabolites in the blood, including amino acids, lipids, and carbohydrates. Over 5,000 metabolites—both identified and unidentified—can be analyzed using AI.

Metabolomics data can be used to:

  • Discover unknown metabolic pathways and their regulation

  • Identify preferred metabolic routes within biological systems

  • Predict the inhibitory effects of substances on metabolic networks

Machine learning, combined with logic-based modeling (including abduction and induction methods), can help predict potential inhibitory side effects of drugs within metabolic systems.

 

Proteomics

Proteomics studies proteins such as cytokines and chemokines, which are secreted into the bloodstream by the immune system and play a vital role in fighting disease. Certain pro-inflammatory protein patterns have been associated with MDS symptoms.

Proteomic data is used to:

  • Train machine learning predictive models

  • Support both regression-based and neural network forecasting

  • Enable prognosis based on protein signatures of MDS or AML patients

Future validation of these models in real-world settings could enable proactive monitoring of patient health.

 

Digital biomarker detection

Digital pathological images combined with multiomics data are used for analysis, achieving up to 93% accuracy in detecting, classifying, and labeling multiple types of cancer.

  • Communication between client software and cloud-based systems is secure

  • Tumor classification reports can be viewed on any device or exported as PDFs

  • Physicians and researchers can make more informed decisions based on detailed tumor insights

  • Continuous feedback helps improve the system over time