Brief Description

This is a term project for TeleMedicine Laboratory course in Spring 2022 led by Prof Manjunatha Mahadevappa at IIT Kharagpur. Chawan Dinesh, Sai Pavan and myself formed as group to develop an web application for Tele-Pathology. Click on this link AI based TelePathology to see the live application deployed in herokuapp.

Aim of the project: The aim of the project is to develop an application that can be used by patients and pathologists for tele-pathology services.

A video demonstration of the project is available on the YouTube. Watch the video to know more about the project.

Features

• Available Features:

  • Patient and Pathologist Sign Up : The users can sign up as a patient or pathologist. In Sign-up page, the user will be asked to choose to be a patient or pathologist. Following the choice, the user will be asked to fill in the specified field. Emergency contact details are compulsory for the patient. Board certificate is compulsory for the pathologist. Once completed, the user will be redirected to the sign-in page.
  • Patient and Pathologist Sign In : As the user is already registered, the user can sign in. The user will asked to sign-in as either patient or pathologist. The user will be asked to enter the email and password. The user will be notified if the email or password is incorrect. The user will be redirected to the home page if the email and password is correct.

  Features for Patients:

  • Dashboard : The dashboard will show the details of the patient, his appointments and pathologist.
  • Appointment Booking : The patient can book an appointment with a pathologist. Patient will choose a pathologist and enters the appointment date and time and with short description of the problem.
  • Pathology Samples : If patient has access to his pathology samples then he can upload the samples. The pathology samples can be uploaded by either patient or pathologist.
  • Diagnostic Comments : The diagnostic comments sent by pathologist will be shown in this page. These comments are unique to the pathology sample.

  Features for Pathologists:

  • Dashboard : The dashboard will show the details of the pathologist, his appointments and patients. It also shows the number of appointments that are pending and completed. In addition, the dashboard will show the number of patients that are waiting for the pathologist. Also shows the statistics of Men Vs Women, Completed Vs Pending etc.
  • Diagnostic Comments : The pathologist will send his feedback on patient samples after detailed analysis.
  • Approve/Reject Appointment Requests : The pathologist can approve or reject the appointment requests.
  • Histological Tissue Segmentation : The pathologist can segment the tissue and perfrom detailed analysis. This would help in easier interpretation of the pathology sample. This is a pretrained HistoSegNet and is deployed on HerokuApp. This feature is not available for the patient.
  • Breast Caner Classification : The pathologist can classify the breast cancer samples at first glance. Later the pathologist can perform detailed analysis and send the results to the patient.

• Upcoming Features:

  • Automatic Reports : As of now the pathologist can only prepares the reports manually and sends them. The report can be made automatically by querying the details from pathologist and can send in a click.
  • Forgot Password : The user can reset the password by entering the email address. A verification email will be sent to the email address to reset the password.

Built Using

The application is developed using the following technologies:

• ReactJS : The frontend is developed using ReactJS.
• NodeJS : The backend is developed using NodeJS.
• ExpressJS : The backend is developed using ExpressJS.
• MongoDB : The database is developed using MongoDB.
• Heroku : The application is deployed on Heroku. The AI models are also deployed on Heroku.
• Python : The deep learning models are kept in production using Python.
• Flask : Flask has been extensively used for web serving the AI models.