PROBLEM STATEMENT: NEXT GENERATION TECHNOLOGY�TITLE: AI &Deep Learning in health care systems

TEAM MEMBERS:

1. PRAVEEN KUMAR.G (CEO & FOUNDER)
2. PRAGNYA.Y (Senior data analyst)
3. GOLOTI PAVANI ACHARY (Senior Software Engineer)

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THE WORLD WE LIVE IN

1. Healthcare organizations of all sizes, types, and specialties are becoming increasingly interested in how artificial intelligence can support better patient care while reducing costs and improving efficiencies.

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• Over a relatively short period of time, the availability and sophistication of AI have exploded, leaving providers, payers, and other stakeholders with a dizzying array of tools, technologies, and strategies to choose from.

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• Healthcare systems are rapidly increasing nowadays and also mistakes have been playing a major role in healthcare organizations. So, For decreasing it we can implement AI, machine learning, deep learning, and semantic computing in healthcare systems to find the solutions for patients rapidly by finding diseases, infections, allergies, cancer, tumors, etc…And giving the right treatments with deep learning.

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• If we are injured by some cause we can find the solution with the use of deep learning and taking first aid before going to a healthcare organization. And finding the best doctors for this treatment with deep learning and AI software.

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• Deep learning also requires less preprocessing of data.  The network itself takes care of many of the filtering and normalization tasks that must be completed by human programmers when using other machine-learning techniques.

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PROBLEM SOLVING

1. PROBLEM TO SOLVE

• Solve problems like MRI scans, X-rays, allergies, and diseases that can be identified by deep learning & ML. And giving the right solutions for healthcare organizations with data analytics and suggesting treatment methods for problems using AI & deep learning.

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• Deep learning can solve problems like Tissue segmentation, chest disease detection, Detection of the standard sagittal plane in pregnancy, Tumours, Tracking the motion of the heart, Alzheimer’s disease identification, Cancer diagnosis, Spine disease diagnosis, and Chronic back pain detection. Etc…

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• Using our deep-learning application we can capture images of the diseases or allergies that can find and it can give solutions and consult the best doctor for the treatment.

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2. People Affected without care

Almost 122 Indians per 100,000 die due to poor quality of care each year, the study said, showing up India’s death rate due to poor care quality as worse than that of Brazil (74), Russia (91), China (46) and South Africa (93) and even its neighbors Pakistan (119), Nepal (93), Bangladesh (57) and Sri Lanka (51).

Repercussions of this problem?

1. Problem worth-solving
• If this is implemented in healthcare organizations. The cost of Accessing health care can be decreased and save millions of life who need treatment at the right time. And identifying the right diseases in the MRI & X-rays etc…And analyzing the best treatment for them. And finding the allergies and symptoms for the disease or infections and giving the right solutions of it. By deep learning & machine learning with AI.

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• What if the problem is not solved
• Half the world lacks access to essential health services, and 100 million are still pushed into extreme poverty because of health expenses. Proper treatment can improve health care services. We cannot break the poverty of healthcare if the problem is not solved. And millions of people are going to suffer without proper healthcare.

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3. India needs doctors

• One of the most critical concerns is the gap in the doctor-patient ratio. According to the Indian Journal of Public, Health India needs 2070000 doctors by 2030. However, a doctor in the government hospital attends to ~11000 patients, which is more than the WHO recommendation of 1:1000.

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SOLUTION

Artificial intelligence (AI), machine learning, deep learning, and semantic computing are used to improve healthcare systems. By first implementing or injecting the data into the ML & DL. When healthcare uploads a problem like MRI scans, x-ray, diseases or allergies, etc…It can be processed and gives the right solutions and treatment as output.

In artificial neural networks (ANNs), the basis for deep learning models, each layer may be assigned a specific portion of a transformation task, and data might traverse the layers multiple times to refine and optimize the ultimate output.  

PROFIT OF THIS PROBLEM

• Developing an Application or software for the healthcare Organisation and providing software with subscription fees that will track the use of this software in their organization. And getting funds from the government sector and implementing them in the government hospital. The monthly subscription fees will be set to 200rs per hospital.

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• In India there are 37,000 hospitals, and the profit for the month is Aprox 74 lakhs per month in the healthcare organization by means of a subscription module

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• And introducing an App for mobiles to identify which kinds of diseases, and allergies and give emergency or first aid solutions and consultation with a doctor on the specified treatment by paying one rupee for a solution. If millions of people use this rather than going to the hospital. We can make a million rupees with this problem.

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How far have you come?

• Till now we have completed a demo model using some libraries like open-cv and thread and NumPy and TensorFlow etc.

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•  we first load the image and tabular data for each sample, which are fed into a CNN model and a fully connected neural network, respectively. Subsequently, the outputs from the two networks will be concatenated and fed into an additional fully connected neural network to generate final predictions.

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• Still it was in the processing state which can need more amount of metadata and need a large amount of data on medical records which are needed to feed into the software.

The competition

• Nowadays machine learning and Al are vastly used in a healthcare organizations with data analytics which will identify diseases through image classification. But it didn’t use in many hospitals so far and it couldn’t track the previous dialysis of the same diseases and it takes time to analyze it.

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• But in deep learning & AI the solution was given in just a few seconds and also it gives the solutions for the treatment on basics of the previous dialysis. So it more useful than existing ones

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• Implementation of Mobile apps can be more useful for the patient or users to get quick first-aid or emergency remedies and it can save millions of life and diseases, allergies and infection can be identified soon with the use deep-learning

Why is it the right solution?

By analyzing how data is filtered through an ANN's layers and how the layers interact with one another, a DL algorithm can 'learn' to make correlations and connections in the data. These capabilities make DL algorithms innovative tools with the potential to change healthcare. Rather than using machine learning software, it is a unique algorithm with keeping track of the previous dialysis

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Sustainability

• This concept of the problem can help many peoples for taking the right treatment for their dialysis of the disease. So healthcare organizations cannot make mistakes in treating patients by deep learning the more imaging algorithms will be analyzed to give the right treatment to the patients those who need medical care and it reduces the cost of attending the healthcare organization

OUR Plan

• To first develop software of deep learning for identifying the major diseases like cancer, tumors, etc… and then further improvising the imaging techniques for a successful business.

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• Short-term plans:- First implement in the rural areas with some hospitals and get feedback from them and then improve it to the next stages. And then identifying allergies, diseases, and infections with the software and then giving the treatments to those who affected

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• Long-term plans:- To identify the MRI scans, X-rays, CT scans, etc…And then give treatment with basics of successful and previous dialysis techniques which couldn’t complicate them of the dialysis.

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• Implementing mobile apps for emergency remedies and consulting the best doctor for it and identifying the kind of allergies, and infections that as caused to them.

OUR Team

• Our team is gathering the images and medical records which can be injected into the algorithm for disease analysis and treating the algorithm with images and information of major successful treatments.

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• Our data analysis team is tasked with collecting, analyzing, and interpreting large amounts of data for feeding the algorithm and building a better AI for querying the data.

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• Our software team is working on Tensorflow and with some python libraries to build a platform for the software for implementing deep learning algorithms like CNNS (Convolutional Neural Networks), RNNS (Recurrent Neural Networks) etc…

NEEDED SUPPORT

• Some technical support in the Bio-medical field to get the medical records and dialysis of the patients for feeding the algorithm to analyze the disease and some software support for doing research & development in deep learning.

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• And support for the processing of GPUs and servers for storing the data and implementing the latest GPUs for this software build-up. One of the primary reasons we are building our own data center with dedicated GPUs is that we rely on medical data such as MRIs and CTs, which can be a lot of data and is sensitive from a privacy perspective. Which can be stored and implemented in the algorithm

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• Financial Support can be needed to build the data centers and buying of the software or new software build-up can cost more for development.

CONCLUSION

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