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At the forefront of healthcare innovation, Biotransport Systems Developments leads groundbreaking research in personalized medicine. Utilizing advanced and robust AI/ML models, we decode disease patterns, extending insights into ocular diseases, cardiovascular disorders, mental health, Alzheimer's, and cancer, shaping a future where data-driven approaches revolutionize healthcare with unparalleled impact.
Our expert insights will keep you informed about new AI-driven healthcare models. Explore the forefront of biotransport systems developments with us today.

Our Extensive Biomedical Database

Diabetes Checker

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Modeling Ocular Phenomena

Transforming Intravitreal Drug Delivery with Deep Learning

Our innovative Physics-Informed Neural Network (PINN) framework accurately predicts the transport of drugs within the vitreous humor by integrating advanced neural network architectures with the physics of intravitreal fluid dynamics.

Glaucoma Detection Through Multi-Modal Integration of Retinal Images and Clinical Biomarkers

We developed a hybrid fusion model integrating Vision Transformers and ResNet50 with clinical biomarker analysis to enhance glaucoma detection accuracy up to 99.4%. This innovative approach combines deep learning with clinical data to provide a comprehensive, highly accurate diagnostic tool, significantly improving early detection and management of glaucoma.

Treatment Plans with AI-Driven Medical Protocols

We utilize advanced AI to create personalized medical protocols, optimizing treatment plans by analyzing patient data and clinical research. 1. Tailored protocol design for individualized treatment plans that enhance patient outcomes. 2. Innovative, data-driven protocols for healthcare professionals that improve standard practices. 3. Comprehensive clinical protocol development to ensure consistency and quality in the treatment pipeline. 4. Advanced data-driven pharmacokinetics protocols to streamline drug development processes.

Modeling Beyond the Eyes

Cardiovascular Screening

We utilize advanced deep learning techniques to revolutionize non-invasive cardiovascular disease prediction through the examination of vascular biomarkers from different ocular regions, including conjunctival and retinal fundus images. Relevant learning approaches adopted with attention-based UNet architectures integrated with a pretrained encoder gave us precision and recall rates above 85% on RFI datasets and above 75% precision with 80% recall on conjunctival image analyses. Furthermore, we are exploring few-shot segmentation with self-supervised transformer architectures to improve adaptability in cases with limited data access, which is a common challenge in this area. The above segmentation lays down a solid basis for morphological assessment and subsequent biomarker extraction, which would be further utilized to connect ocular and cardiovascular biomarkers to evaluate the comprehensive risk of CVD.

Alzheimer’s

Our advanced product pipeline for improving Alzheimer's disease detection combines the power of computer vision and biomedical innovation. Utilizing Optical Coherence Tomography scans, our platform is built on the EfficientNetB6 model, trained on a comprehensive dataset of nearly 84,000 images across categories such as CNV, DRUSEN, DME, and Normal. Achieving an accuracy of 97% on test data, it surpasses traditional models like ResNet50 and sets a new benchmark in precision diagnostics. Designed for scalability and future growth, our platform enables optic nerve segmentation and the tracking of retinal layer changes. This innovation opens new possibilities for early detection and monitoring of neurodegenerative conditions, empowering clinicians with deeper insights to improve patient outcomes.

Mental Health

Utilizing deep learning techniques, we aim to predict major mental health disorders, including Schizophrenia, Conduct Disorder, and Autism. The ABCD study dataset serves as a foundation, integrating advanced methodologies like neuroimaging and genomic analysis for comprehensive model training. The interdisciplinary approach encourages collaboration across diverse fields, with a primary objective of advancing early diagnosis and personalized treatment through deep learning. Additionally, a commitment to transparency is demonstrated by providing an open-source pre-trained model, with anticipated future applications in MRI-based diagnosis to enhance overall diagnostic accuracy.

AI Assistant for Visually Impaired People

We will develop multiple smart phone applications to gain accessibility for visually impaired people.

Customized Ocular Software Solutions

At Biotransport Systems Developments, we specialize in developing customized ocular software solutions tailored to the unique needs of clinics. Our software enhances operational efficiency by streamlining workflows and reducing inefficiencies.

Multi-modal Image Processing and Analysis

Our Multi-Modal Image Processing and Analysis platform provides key insights into disease mechanisms and accelerates drug development by analyzing diverse imaging data. It empowers clinicians and researchers to develop more targeted and effective treatments. 1. Developing new biomarkers. 2. Feature enhancing for image database creation. 3. Highlight key pathologies on imaging data.

Postvitreal Detachment (PVD)

At Biotransport Systems Developments, we will evaluate the factors for high-risk patients in PVD (retinal tear). We will develop ML/DL methods to identify and determine pathological features in vitreo-retinal disorders to evaluate the quality of training datasets and simultaneously provide diagnoses for PVD status and the necessity of further examination and/or treatment. Such algorithms would be crucially important in predicting PVD occurrence and progression, so that timely and adequate treatment can be instituted.

Iridology: Big Scale Disease Detection

Harnessing advanced AI and image processing, Biotransport Systems Developments transforms iridology into a scalable diagnostic tool for early disease detection. Our platform enables non-invasive, accurate, and cost-effective healthcare solutions to enhance patient outcomes.

Dry Eye

This innovative work aims to revolutionize the diagnosis of dry eye syndrome by developing a cutting-edge method that effectively distinguishes healthy eyes from those affected by dryness by focusing on critical biomarkers such as tear break-up time, tear meniscus height, and meibomian gland structure. This way we can detect subtle structural differences that are often overlooked. The integration of DINOv2 embeddings with ALPNet unlocks the potential for few-shot segmentation, enhancing feature extraction and leading to accurate identification of dry eye disease.

CONSULTING

Anita Penkova

CEO and Founder

Dr. Penkova's focus is on developing patient data-driven algorithms to derive patient-specific physiological information from extensive imaging data, ultimately reducing diagnostic and therapeutic errors and advancing personalized care. These efforts involve extracting valuable information from inner surface eye imaging to diagnose non-ocular conditions such as diabetes, cardiovascular disorders, and mental illness. Our developed codes/software also includes the development of therapeutic drugs for glaucoma patients, which are informed by experimentation and modeling of glaucoma-related fluid transport. My expertise encompasses ML, DL, computer vision, ocular fluid dynamics and transport, oxygen transport processes in the eye, the effect of shear flow on protein clustering and aggregation, and studies on glaucoma-related fluid transport.

Dr. J. Sebag

VMR Institute

He is presently Senior Research Scientist at the Doheny Eye Institute in Pasadena and Professor of Clinical Ophthalmology at UCLA. As of 2025, Dr. Sebag has authored 273 academic publications and delivered an equal number of lectures/abstracts on various aspects of vitreo-retinal health and disease. Dr. Sebag’s Google Scholar H-index = 59, with 13,742 citations to his work. His 2014 textbook (https://link.springer.com/book/10.1007%2F978-1-4939-1086-1) on vitreous has had 189,000 downloads and was translated into Chinese.

Qilong Pan

Partner

Qilong Pan specializes in machine learning engineering and software development, with expertise in ocular health diagnostics and predictive analytics. His current research involves developing innovative ocular ultrasound analytics tools aimed at constructing detailed vitreous liquefaction maps, integrated with advanced Physics-Informed Neural Networks (PINNs) for accurate prediction of intravitreal drug flow dynamics. Additionally, Qilong successfully developed an advanced U-Net-based neural network capable of diagnosing diabetes from ocular images with accuracy exceeding 95%. Beyond his research contributions, he actively collaborates on software development projects, transforming cutting-edge research findings into practical healthcare products.

Venkata Saai Praneeth Thota

Partner

Venkata Saai Praneeth Thota contributes to biomedical imaging research, building models like TransU-Net and GCNNs for vascular segmentation to identify cardiovascular biomarkers. Previously, at Share Ventures, he developed AI agents for market intelligence in the human performance industry using hybrid RAG Agents. His core expertise spans machine learning, LLMs, retrieval-augmented generation, biomedical AI, and time-series analysis, with publications in venues like IEEE TNSM. He is an AI Research Engineer at Modlee, a dynamic startup innovating with LLMs and deep learning for domain-specific intelligence solutions.

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