Tag: lung disease

New AI Diagnostics for Lung Diseases

AI and IoT based Diagnostic Device for Chronic Obstructive Pulmonary Diseases developed by IIT Kharagpur

Chronic Obstructive Pulmonary Disease (COPD) is a common chronic respiratory disease caused by exposure to harmful gases and particulate matters, with a high health burden on the country’s healthcare services and society. For long, the medical community has been depending on patient history and clinical symptoms for disease diagnosis, which often prevents early detection of the disease and advancing of the disease adds to the medical bill through frequent hospitalizations. 

Researchers at IIT Kharagpur have developed an affordable diagnostic intervention for Chronic Obstructive Pulmonary Disease based on the internet-of-things medical devices (IoT-MD) integrated with AI. [Download Journal Paper]

At the Organic Electronics Laboratory (ORELA), Department of Physics, IIT Kharagpur, Prof. Dipak Kumar Goswami and his research team have developed SenFlex.T, a smart mask synced with an android monitoring app through Bluetooth, that can continuously monitor breathing patterns, rate, heart rate, oxygen saturation level in blood. The app is connected to a cloud computing server, where artificial intelligence (AI) has been implemented to predict the severity of COPD through machine learning (ML). 

“SenFlex.T can be used at home by patients without having to visit diagnostic centres as against the current practice. This will also address the critical issue of addressing COPD at an early stage and by means of advanced healthcare technology, a boon for both patients and the overall healthcare system,” explained Prof. Goswami. 

Chronic Obstructive Pulmonary Disease has been a top cause of death, second to only deaths due to heart diseases. In 2017 it claimed about 1 million lives in India. In October 2019, health experts, at a medical convention, confirmed that COPD claims more lives than AIDS, TB, Malaria, Diabetes all put together. The threat from COPD has become more acute under the COVID situation, with increased comorbidity rates. A recent survey confirmed that the severity and mortality rates among COPD patients to be affected by the COVID-19 virus are over 63%. Moreover, the patients affected in the COVID-19 virus, which is right now over 4 million people in India and 27 million in the world, are more susceptible to build up various lung disorder-related diseases like COPD, Asthma etc. 

“It was crucial for health-tech researchers to develop a diagnostic intervention for Chronic Obstructive Pulmonary Disease. Spirometry, the gold standard test to diagnose obstructive airway diseases like asthma and COPD, is often avoided due to the unavailability of the equipment, difficulty in data interpretation and the cost of the tests. This challenge and the criticality strongly motivated us to develop an AI-based system, that can overcome the problem of interpreting the results and be accessible not only for the doctors but also for the patients,” said Prof. Goswami. 

SenFlex.T smart mask contains a highly sensitive, flexible temperature sensor along with a Bluetooth based measuring electronics. The sensor system can continuously monitor the temperature changes of inhaled and exhaled air during breathing and record the breathing pattern. The temperature sensor has a resolution of 4.3 mK and about 25 ms response time. Further, a commercially available pulse oximeter has been integrated with the sensor system to monitor the oxygen saturation level during breathing.

The patient data is uploaded automatically to the cloud server through the mobile app (SenFlex), where it is processed by means of AIML, and reports made available on the app and for doctors’ consultation. 

The innovation has been reported in the international journal ACS Applied Materials & Interfaces [Download Paper]. The researchers have also filed a patent for the innovation and are ready for commercialization. The product cost has been estimated at about ₹ 2,500/-.

Cite Paper:  ACS Appl. Mater. Interfaces 2019, 11, 4, 4193–4202
Publication Date:December 31, 2018
https://doi.org/10.1021/acsami.8b19051

Media Coverage:

Project Information: Prof. Dipak Goswami, dipak@phy.iitkgp.ac.in; 

Institute Information: Prof. B N Singh, Registrar, registrar@hijli.iitkgp.ac.in;

Media Outreach: Shreyoshi Ghosh, shreyoshi@adm.iitkgp.ac.in

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Smart Diagnostics for Pulmonary Healthcare

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Researchers at the Dept. of Electronics and Electrical Communications Engineering at IIT Kharagpur have developed a decision support system to diagnose malignant and other diseased tissues in the lungs. While one system can refer to CT scan images to detect lung nodules and test them for the possibility of malignancy, a second software can detect Interstitial Lung Disease (ILD) patterns in chest HRCT images.

“Biopsy especially in the lungs is a critical process, hence conducted only after initial medical analysis is done by expert radiologists. The developed systems use noninvasive and comparatively affordable methods of image analysis that would aid the radiologists to identify malignancy by reading growth in the lung nodules. The other system will help identify interstitial disease patterns in HRCT images depicting the lung tissue texture,” explained lead researcher Prof. Sudipta Mukhopadhyay.

“The novelty of the system lies in its India-centric reference point i.e. the medical image scan database used for reference is sought from the Indian patient population. We worked with Prof. Khandelwal and his team from PGIMER Chandigarh for data ground truth and clinical data. Also, foreign database such as LIDC-IDRI and MedGIFT ILD database has been used. The biopsy cases were primarily taken from PGIMER,” explained researcher Shrikant Mehre.

The malignancy detecting tool detects the lung nodule, segment the nodule, and provides a way to modify segmentation, retrieve similar nodules from the database with their report and assess the chance of malignancy of the query nodule based on the retrieval results. The ILD tool is developed by incorporating feedback from expert radiologists to make it easy to use for non-tech savvy clinicians. The software is equipped with necessary modules such as automatic segmentation of lung boundary and pathological region within lung area, provision to modify the boundary, retrieving similar segments from the database with their report and assess the probability of the pathological segment to be a particular ILD category based on the retrieval results. The mapping of disease is performed by doctors based on the ILD pattern and clinical inputs.

“We have successfully tested both software systems at AIIMS Delhi. Prof. Ashu Seith Bhalla and her team provided the neutral test site required for the validation. Currently, lung nodule detection rate and classification rate is 86% and 87%, respectively, and the success rate for ILD classification is 84%. We are working towards further improvements in order to conduct clinical trials on bigger sample sizes,” said another researcher Mandar Kale.

With the growing cases of cancer and other respiratory diseases in India, the need for skilled radiologists is expected to grow exponentially in near future. Budding radiologists will be highly benefited in learning from previous images stored in Picture Archival and Communication System (PACS) and reports in Radiological Information System (RIS) on their own and to help practicing radiologists in differential diagnosis using the CBIR based Computer Aided Diagnosis (CAD) system.

The research has been reported in more than 13 international journals and 19 international conferences through its various stages of progress.