Ideaz

In India, getting a diabetes diagnosis is frequently a stressful and delayed process. There may be a lot of stress during the time between identifying the first symptoms, making an appointment with a doctor, and eventually receiving the results of a blood test. Many people put off this important step because they find it inconvenient or are afraid of needles, which results in a large number of undiagnosed or pre-diabetic cases in our communities.

AI from Diabetes Health is here to change that. We are creating a concise, targeted mobile application that offers a quick, initial Type 2 diabetes risk assessment. Our app provides an immediate understanding of a user's possible risk by utilizing a machine learning model that evaluates basic, non-invasive information such as a user's height, weight, and BMI. Our goal is to remove people's reluctance to get tested in the first place and enable them to take the vital first step toward promptly seeking professional medical advice.

The Issue:

Anxiety and Diagnostic Delays: Diabetes cannot be tested instantly using the conventional method. Waiting for blood test results can be extremely stressful and postpone the start of necessary lifestyle changes or medical consultations.

Testing Hesitancy: Because blood tests can be uncomfortable, expensive, or inconvenient, many people steer clear of preventative screening. This results in a sizable population that is undiagnosed and ignorant of their risk.

Absence of an Accessible First Step: Self-concern and clinical diagnosis are not the same thing. People need a quick, easy, and private tool to help them determine whether seeing a doctor should be their top priority.

The "Diabetes Health AI" mobile application is our MVP solution.

Our MVP:

 It is simple, intuitive mobile application with a single primary purpose:

Instant Risk Assessment: After launching the app, the user inputs their age, height, and weight, among other basic information. Their BMI is automatically determined by the app, which then feeds the results into our in-house machine learning model.

Clear, Simple Results: Within seconds, the app displays a clear, easy-to-understand preliminary risk assessment, categorized into levels such as "Low Risk," "Medium Risk," or "High Risk."

Practical Advice: In addition to the outcomeThe app makes it clear that it is a screening tool and not a diagnostic one, and it strongly advises talking with a doctor about the results.

Important Note: The Diabetes Health AI app is not a replacement for a medical diagnosis; rather, it is a preliminary risk indicator. It should not be used in place of professional medical evaluation and testing; rather, it should be used to encourage users to do so.

Technical Details:
Hardware elements:

Smartphone of the user: The only hardware needed for the application is a typical smartphone because it is fully software-based.

Core Predictive Model (Classification Model): AI Components

A machine learning model, most likely a Support Vector Machine (SVM) or Logistic Regression model, is at the core of our application. It was selected due to its demonstrated accuracy in binary classification tasks.

To identify trends between input features—Age, Height, Weight, and BMI—and the risk of diabetes, the model is trained on well-known, anonymized medical datasets (like the Pima Indians Diabetes Database).

Mechanism of Continuous Learning:

By periodically retraining, the model is intended to "learn from its mistakes" and get better over time.

Users will have the option to report the results of their official clinical diagnosis through an optional, anonymous feedback loop. The model is routinely retrained and improved using this fresh, validated data, improving its accuracy and dependability for all users.

The information gathered will be crucial for:
Enhancement of Iterative Models: Building a strong feedback system is the main application of the data. We can improve the algorithm's accuracy, lower its margin of error, and increase the precision of the risk predictions with each new dataset.

Public Health Insights: Anonymized aggregate data can be used to find associations between a user's physical characteristics (such as particular BMI ranges) and their risk of developing diabetes. This information is useful for public health research.

Targeted Awareness: Public health organizations can develop more successful and focused diabetes prevention awareness campaigns by having a better understanding of the general risk profiles of our user base.

 Conclusion :

Diabetes Health AI aims to get more people to see doctors sooner rather than to replace them. Our goal is to make the first step toward health awareness quick, easy, and stress-free. Millions of people will be able to take immediate control of their health by removing the initial barrier of testing hesitancy and establishing a link between professional diagnosis and personal concern

In Hyderabad, our street dogs face a daily struggle for survival. Our current approach to their welfare is often a guessing game, driven by a lack of real data. We see the aggression, the suffering, and the overpopulation, but we've been tackling it with our hands tied.

Paw-Print is here to change that. We want to develop a small, smart device ,a motion-sensor camera attached to a humane gravity food and water feeder. We'll place them strategically in communities to capture crucial data on dog populations, their movements, and even their family lines with the help of local governments, NGOs and communities.

The Problem:

1. Lack of Accurate Data Regarding the population and other factors related to dogs
2. Inbreeding and Genetic Issues which are one of the main causes of aggression in stray dogs which go hand in hand with environmental factors
3. Without a clear understanding of population hotspots, migration routes, and dog-specific needs, existing animal welfare efforts are often a "shot in the dark," leading to waste and leaving countless dogs unassisted.

Our MVP Solution:

The "Paw-Print" Smart Feeder System Our MVP is a discreet, weatherproof device to be deployed in targeted localities. Each unit combines:

• Automated Food and Water Dispensing
• Motion-Sensor Camera which uses AI to track and detect the dogs and counts the population
• Geotags which helps in gathering the data at each data point and transmits it to our central database in real time.

Technical Information:

Our Device Contains :

Hardware components:

1. Camera: A low-power, high-resolution camera with an infrared (IR) night vision sensor to capture data 24/7. An IR filter is crucial for accurate daytime color and a clear night-time picture.
2. Processor: A NVIDIA Jetson Nano processor, chosen for its ability to run AI models on the edge, meaning data is processed locally before being sent, saving bandwidth and power.
3. Power Supply: Solar powered and rechargeable battery to store the power and use it during cloudy days
4. Gravity Feeder: Food and Water feeder which needs to be refilled by the volunteers over a period of time
5. Enclosure: A robust, tamper-proof, and waterproof casing to protect the electronics from the elements and from curious animals.

AI Components:

The core of this idea lies in the AI model's ability to identify individual dogs. This requires a multi-stage approach:

1. The first layer is Object detection model (YOLO orSSD) to detect and differentiate other things and animals from dogs
2. Reidentification Model is key layer to prevent recounting of the dogs. Thismodel will analyze the detected dog's image, focusing on unique features like fur color, patterns, size, and shape. It then generates a unique "feature vector" for that dog. When a new image of a dog is captured, the model compares its feature vector to all existing vectors in the database. If there's a match above a certain confidence threshold, it's identified as the same dog. If not, it's logged as a new dog.
3. Data Labeling of dogs. To train this model, we will need a large, labeled dataset of street dog images, a key challenge for this idea. To encounter this problem we can start with publicly available datasets and then collect and label our own data from the pilot project.

The data collected will be invaluable for:

• This Data is then used to create the appropriate number of shelters across the city where they are provided with basic amenities, health care and neutered when needed.
• Creating a real-time, high-density map of dog populations across the city, identifying hotspots and density changes, Breeding seasons for dogs , Movement and Migration tracking of the dogs and hierarchy structure of the dogs
• This Data will be used to create a Pedigree chart and Genetic analysis. Our AI will analyze unique markings, fur patterns, and other features to differentiate individual dogs, allowing us to track family lines and identify areas with high rates of inbreeding.

Conclusion:

This isn't just about counting dogs. It's about giving them a voice. The data we collect will allow us to see their struggles and needs with unprecedented clarity. By working with local government and community leaders, we will transform guesswork into a strategic, compassionate plan.

Paw-Print's mission is simple: to create a foundation of knowledge that will help us build a future where every street dog in Hyderabad is seen, cared for, and has a safe place to call home.