## Inspiration

Our solution was named on remembrance of Mother Teresa

## What it does

Robotic technology to assist nurses and doctors in medicine delivery, patient handling across the hospital, including ICU’s. We are planning to build an app in Low code/No code that will help the covid patients to scan themselves as the mobile app is integrated with the CT Scanner for doctors to save their time and to prevent human error . Here we have trained with CT scans of Covid and developed a CNN model and integrated it into our application to help the covid patients. The data sets are been collected from the kaggle and tested with an efficient algorithm with the efficiency of around 80% and the doctors can maintain the patient’s record. The beneficiary of the app is PATIENTS.

## How we built it

Bots are potentially referred to as the most promising and advanced form of human-machine interactions. The designed bot can be handled with an app manually through go and cloud technology with e predefined databases of actions and further moves are manually controlled trough the mobile application. Simultaneously, to reduce he workload of doctor, a customized feature is included to process the x-ray image through the app based on Convolution neural networks part of image processing system. CNN are deep learning algorithms that are very powerful for the analysis of images which gives a quick and accurate classification of disease based on the information gained from the digital x-ray images .So to reduce the workload of doctors these features are included. To know get better efficiency on the detection I have used open source kaggle data set.

## Challenges we ran into

The data source for the initial stage can be collected from the kaggle and but during the real time implementation the working model and the mobile application using flutter need datasets that can be collected from the nearby hospitality which was the challenge.

## Accomplishments that we're proud of

* Counselling and Entertainment
* Diagnosing therapy using pose detection
* Regular checkup of vital parameters
* SOS to doctors with live telecast
-Supply off medicines and foods

## What we learned

* CNN
* Machine Learning
* Mobile Application
* Cloud Technology
* Computer Vision
* Pi Cam Interaction
* Flutter for Mobile application

## Implementation

* The bot is designed to have the supply carrier at the top and the motor driver connected with 4 wheels at the bottom.
* The battery will be placed in the middle and a display is placed in the front, which will be used for selecting the options and displaying the therapy exercise.
* The image aside is a miniature prototype with some features
* The bot will be integrated with path planning and this is done with the help mission planner, where we will be configuring the controller and selecting the location as a node
* If an obstacle is present in the path, it will be detected with lidar placed at the top.
* In some scenarios, if need to buy some medicines, the bot is attached with the audio receiver and speaker, so that once the bot reached a certain spot with the mission planning, it says the medicines and it can be placed at the carrier.
* The bot will have a carrier at the top, where the items will be placed.
* This carrier will also have a sub-section.
* So If the bot is carrying food for the patients in the ward, Once it reaches a certain patient, the LED in the section containing the food for particular will be blinked.
## Inspiration

In 2012 in the U.S infants and newborns made up 73% of hospitals stays and 57.9% of hospital costs. This adds up to $21,654.6 million dollars. As a group of students eager to make a change in the healthcare industry utilizing machine learning software, we thought this was the perfect project for us. Statistical data showed an increase in infant hospital visits in recent years which further solidified our mission to tackle this problem at its core.

## What it does

Our software uses a website with user authentication to collect data about an infant. This data considers factors such as temperature, time of last meal, fluid intake, etc. This data is then pushed onto a MySQL server and is fetched by a remote device using a python script. After loading the data onto a local machine, it is passed into a linear regression machine learning model which outputs the probability of the infant requiring medical attention. Analysis results from the ML model is passed back into the website where it is displayed through graphs and other means of data visualization. This created dashboard is visible to users through their accounts and to their family doctors. Family doctors can analyze the data for themselves and agree or disagree with the model result. This iterative process trains the model over time. This process looks to ease the stress on parents and insure those who seriously need medical attention are the ones receiving it. Alongside optimizing the procedure, the product also decreases hospital costs thereby lowering taxes. We also implemented a secure hash to uniquely and securely identify each user. Using a hyper-secure combination of the user's data, we gave each patient a way to receive the status of their infant's evaluation from our AI and doctor verification.

## Challenges we ran into

At first, we challenged ourselves to create an ethical hacking platform. After discussing and developing the idea, we realized it was already done. We were challenged to think of something new with the same amount of complexity. As first year students with little to no experience, we wanted to tinker with AI and push the bounds of healthcare efficiency. The algorithms didn't work, the server wouldn't connect, and the website wouldn't deploy. We persevered and through the help of mentors and peers we were able to make a fully functional product. As a team, we were able to pick up on ML concepts and data-basing at an accelerated pace. We were challenged as students, upcoming engineers, and as people. Our ability to push through and deliver results were shown over the course of this hackathon.

## Accomplishments that we're proud of

We're proud of our functional database that can be accessed from a remote device. The ML algorithm, python script, and website were all commendable achievements for us. These components on their own are fairly useless, our biggest accomplishment was interfacing all of these with one another and creating an overall user experience that delivers in performance and results. Using sha256 we securely passed each user a unique and near impossible to reverse hash to allow them to check the status of their evaluation.

## What we learned

We learnt about important concepts in neural networks using TensorFlow and the inner workings of the HTML code in a website. We also learnt how to set-up a server and configure it for remote access. We learned a lot about how cyber-security plays a crucial role in the information technology industry. This opportunity allowed us to connect on a more personal level with the users around us, being able to create a more reliable and user friendly interface.

## What's next for InfantXpert

We're looking to develop a mobile application in IOS and Android for this app. We'd like to provide this as a free service so everyone can access the application regardless of their financial status.
## Inspiration:

The human effort of entering details from a paper form to a computer led us to brainstorm and come up with this idea which takes away the hassle of the information collection phase.

## What it does?

The application accepts an image / pdf of a medical form and analyses it to find the information which is then stored in a database

## How we built it?

We applied Optical Character Recognition techniques to evaluate data which was present in the form, and then formatted it to provide valuable information to the nurse/doctor

## Challenges we ran into:

Each person has his/her own way of writing and thus it was difficult to identify the characters

## Accomplishments that we're proud of:

We could come up with an MVP during these 36 hours by implementing a solution involving new technologies / workflows.

## What we learned?

We learned about the presence of several APIs providing OCR functionality which might differ based on the optimisation, also we learned more about client-server architecture in a way the patient's (client) request reaches nurse/hospital (server) asynchronously.

## What's next for Care Scan?

We would love to take this idea forward and integrate the solution with different services and regulations to provide an enriching experience to the user including but not limited to the scope of machine learning, NLP and event driven architectures.

## Link to Codebase

<https://github.com/Care-Scan>