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In the rapidly evolving landscape of healthcare, technological advancements are essential for improving patient outcomes, enhancing surgical precision, and ensuring safety measures. Our solutions empower healthcare providers to deliver superior care through cutting-edge technologies. We provide state-of-the-art solutions for pill recognition, surgical assistance, PPE monitoring, and cancer screening, powered by GenAI and AI/ML technology. Computer vision can assist with common, repetitive tasks and, in some cases, replace human involvement altogether. Images from ultrasounds, x-rays, endoscopy, thermography, MRIs, and security feeds can all be used to train a computer vision model. And we can label thisdata ourselves or outsource these tasks to labelling services.
Pill Recognition
Utilize AI algorithms for accurate pill identification and medication management.
Improve patient safety and adherence through automated pill recognition technology.
Assist surgeons with real-time data and visualization tools for enhanced precision during procedures.
Improved surgical outcomes and reduce risks with advanced surgical assistance technology.
Surgical Assistance
PPE Monitoring
Monitor healthcare workers' adherence to PPE protocols using AI-based monitoring systems.
Ensure safety and minimize the risk of infection in healthcare settings through real-time monitoring.
Implement AI-driven screening tools for early detection and diagnosis of cancer.
Improve patient outcomes through timely and accurate cancer screening technologies.
Cancer Screening
Why Choose Us?
Improved
Patient Safety
Ensure accurate medication management and reduce medication errors with pill recognition technology.
Enhanced
Surgical Precision
Assist surgeons with real-time data and visualization tools for efficiency and better outcomes.
Safety
Compliance
Monitor PPE usage to ensure compliance with safety protocols and minimize the risk of infection.
Early Disease
Detection
Detect cancer at the early stages, leading to better treatment outcomes and more survival rates.
Efficiency and
Accuracy
Increase efficiency and accuracy in the medical field and healthcare delivery through AI-driven solutions.
See real impact:
Healthcare solutions
transforming patient
care, surgeries,
and safety
Case Study: Ischemic Stroke Detection Mobile App
Introduction
In this case study, we explore the development and performance of a mobile app designed to detect ischemic strokes. The app utilizes both image and voice data, feeding them into separate neural network models for prediction. Our goal was to create an accurate and user-friendly tool that could assist medical professionals in diagnosing ischemic strokes promptly.
Background
Ischemic strokes occur when blood flow to the brain is blocked due to a clot or other vascular obstruction. Early detection is crucial for effective treatment and minimizing long-term damage. Our app aims to provide a rapid and reliable diagnosis by analyzing both visual and auditory cues.
App Features
- User Interaction: Users speak a few sentences while the app records video. The spoken sentences serve as input for the voice neural network model. Simultaneously, the app captures images of the user’s face during speech.
Neural Network Models: - Image Neural Network Model:
Trained on a large dataset of labeled brain images (including ischemic stroke cases).
Predicts the likelihood of ischemic stroke based on facial features extracted from the images.
Achieves an impressive accuracy of 96%. - Voice Neural Network Model:
Trained on audio samples from stroke patients and healthy individuals.
Analyzes speech patterns, tone, and other vocal characteristics.
Achieves an accuracy of 95% in identifying stroke-related patterns.
Integration:
The app seamlessly integrates the predictions from both models. If either model indicates a high likelihood of stroke, the app alerts the user to seek immediate medical attention.
Implementation
- Data Collection: Collected a diverse dataset of stroke patients’ images and voice recordings. Ensured privacy and consent compliance.
- Model Training: Preprocessed images (face detection, normalization) and audio (feature extraction). Trained separate neural networks using deep learning frameworks (TensorFlow). Fine-tuned hyperparameters to optimize accuracy.
- App Development: Developed a user-friendly interface for recording speech and capturing images. Integrated neural network models into the app’s backend. Ensured real-time processing for quick results.
Conclusion
Our ischemic stroke detection mobile app demonstrates the potential of combining image and voice data for accurate diagnosis. With a 96% accuracy rate for image-based predictions and 95% accuracy for voice-based predictions, the app holds promise in improving stroke management and patient outcomes. Further research and collaboration with healthcare institutions are essential for widespread adoption and continuous improvement.
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