IIT Jodhpur Researchers Have Found Low-cost AI Algorithm to Detect Cataracts

A team of researchers from the Indian Institute of Technology (IIT)

Jodhpur has found that eye images acquired by low-cost near-infrared (NIR) cameras can aid in low design costs, ease of use, and practical solutions for cataract detection. In short, the AI based solution can be useful in cataracts or other operations making them for affordable for people.

“Known as MTCD, the proposed multitask deep learning algorithm is

inexpensive and results in very high levels of accuracy. This research presents a deep learning-based cataract detection method that involves iris segmentation and multitask network classification. The proposed segmentation algorithm efficiently and effectively detects non-ideal eye boundaries,” claims the researchers.

In traditional methods, cataracts are mainly detected through fundus images, where image acquisition is costlier and needs experts to handle the fundus cameras. The AI based solution with low-cost imaging devices can make it more accessible and inexpensive. It is also cost-effective as low-cost NIR cameras that are used in place of costly ophthalmoscopes. The proposed method can be used in rural settings where the availability of doctors is limited, claims the research.

The research was conceptualised by Dr Mayank Vatsa and Dr Richa Singh from the Image Analysis and Biometrics (IAB) Lab at IIT Jodhpur. They were supported by various UG and Ph.D students at the lab – Mahapara Khurshid, Yasmeena Akhter, Rohit Keshari, Pavani Tripathi, and Aditya Lakra.

Speaking about the research, Dr Richa Singh, professor, department of computer science and engineering, IIT Jodhpur, said, “Currently, a large number of patients with cataracts have to visit secondary and tertiary care centres. The availability of such a solution can assist doctors at the primary health care centres in helping such patients.”. Dr. Mayank Vatsa, Professor, Department of Computer Science and Engineering, IIT Jodhpur, further added, “We are extending this research to include both cataract and diabetic retinopathy in the solution and have collaborated with multiple hospitals in the country for domain expertise, data collection, and validation of the solution.”

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