
SGPGI gets AI-enabled cardiac imaging technology for precision angioplasty
1
2
Lucknow: People in the city will now be able to avail the advantage of
precision angioplasty
as the Sanjay Gandhi Postgraduate Institute of Medical Sciences (
SGPGIMS
) has equipped its cardiology department with an AI-powered intravascular
optical coherence tomography
(OCT) system.This medical device allows interventional cardiologists to understand the internal structure of the plaques formed inside arteries, like the back of their hand, which eventually cause cardiac troubles.The exact understanding helps tailor their procedure to the specific needs of the patient, which is known as precision angioplasty.
"This is the first of its kind paraphernalia in the capital. We commissioned it on May 5. So far, we have used it successfully on 15 patients," said Prof Aditya Kapoor, head of cardiology department, adding that his colleagues Prof Satyendra Tiwari, Prof Rupali Khanna, Prof Naveen Garg, and Dr Ankit Sahu have acquired the skills to use the setup."Till now, plaque morphology was studied manually. For this, a catheter containing a micro camera was inserted into the artery to gather the required information. However, the pictures generation were of low resolution. But now, AI-OCT provides live and high-quality images of the plaque structure at the click of a button within a fraction of a second," Prof Kapoor said.Prof Tewari, who is also a senior functionary of the Cardiological Society of India, said that the technology was a game changer in several ways. "The plaque is a build-up of fatty substances, cholesterol, cellular waste products, calcium, and fibrin (coagulant). The exact details will help in understanding acute myocardial infarction better and allow us to choose the best-suited stent for patients. It will also help us see if the stent inserted was deployed perfectly or not, which is advantageous for the patients," he said.The two experts pointed out that the medical marvel would help doctors acquire a better understanding of the problem of coronary artery disease, which hits Indians at a much younger age and with higher intensity. "It was observed that Indians have a higher percentage of fat in their plaque buildup, which causes greater damage in case of an attack. Precise imaging will help in high-quality treatment and outcomes," said Prof Kapoor.Talking about the technology, they said that the system integrated fractional and relative flow reserve (assessment of coronary flow) with 3D angio-co-registration (a technique providing simultaneous angiographic views)."By utilising AI algorithms instead of manual analysis, the technology can more accurately evaluate plaque structure, calcification, vessel size, stent positioning, and integrate this information with angiography data," they said.The machine also offers high-resolution real-time 3D reconstruction, 3D volumetric imaging, and simultaneous angio-OCT display, significantly enhancing procedural accuracy and patient treatment outcomes.Director, Prof RK Dhiman, hailed the inclusion of the technology and was amazed by its rapid and precise decision-making capabilities, extending full support to the team.
Hashtags

Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles


Hans India
an hour ago
- Hans India
India's first astronaut to International Space Station: Shubhanshu Shukla set to launch with Axiom space
India is set to make history as Shubhanshu Shukla becomes the first Indian astronaut to travel to the International Space Station (ISS). Scheduled for launch on Tuesday from the US, the mission is part of a private collaboration with Axiom Space, utilizing a SpaceX capsule. Shukla, 39, is an experienced Indian Air Force fighter pilot. He joins a four-person international crew that includes mission commander Peggy Whitson, a former NASA astronaut now with Axiom, Polish astronaut Sławosz Uznański-Wiśniewski from the European Space Agency, and Hungary's Tibor Kapu. Together, they will spend 14 days aboard the ISS conducting 60 scientific experiments covering microgravity, Earth observation, life sciences, and materials research. Shukla is the third astronaut of Indian origin to reach space, following Rakesh Sharma in 1984 and Kalpana Chawla, who flew as a US citizen before tragically losing her life in the 2003 Columbia disaster. Reflecting on his mission, Shukla said, 'Even though I am traveling alone, this is the journey of 1.4 billion Indians. I hope to inspire a new generation.' The Indian government, through its Department of Space, described the mission as a 'defining chapter' for the country. With a reported cost of over $60 million, the mission symbolizes India's increasing presence in the global space arena. India's space ambitions are accelerating. Prime Minister Narendra Modi has pledged to send an Indian to the Moon by 2040, and ISRO is preparing its own human spaceflight program—Gaganyaan—set for 2027, with Shukla a potential candidate. His earlier training at Russia's Yuri Gagarin Cosmonaut Training Center and ISRO's Bengaluru facility has primed him for this historic role. This landmark mission reflects India's growing capabilities in space exploration, positioning it alongside global leaders while maintaining cost efficiency and innovation.


India Today
4 hours ago
- India Today
Dementia, Alzheimer's or just brain fog? Here's how to tell the difference
For years, conditions like dementia and Alzheimer's disease were often brushed aside in India as issues of "old age" or even mistaken for personality with rising awareness and better healthcare access, families and doctors are starting to recognise these as medical concerns reported closer to home that deserve timely to the Alzheimer's and Related Disorders Society of India (ARDSI), more than 5.3 million Indians are currently living with dementia, and this number is expected to triple by 2050 due to the country's ageing DEMENTIA VS ALZHEIMER'S: WHAT'S THE DIFFERENCE?While people often use the terms interchangeably, dementia and Alzheimer's disease are not the same is an umbrella term, used to describe a group of symptoms that affect memory, thinking, and social abilities severely enough to interfere with daily life. It happens when cells in the brain slowly begin to die. In dementia, symptoms can include forgetfulness, confusion, having trouble speaking, having difficulty in comprehending conversations, pulling away, and change in is the most common type of dementia, accounting for 60% to 70% of all dementia cases in the of dementia as the disease and Alzheimer's as one of the main are other forms of dementia too, like vascular dementia, frontotemporal dementia, and Lewy body dementia. Each has different causes and Alzheimer's disease, the brain undergoes physical changes, including the build-up of abnormal proteins that slowly damage and kill brain leads to progressive memory loss, confusion, and changes in personality and dementia can strike in the 60s and above, symptoms are now striking as young as the 40s, known as young-onset DOES BRAIN FOG FIT IN?Unlike dementia and Alzheimer's, brain fog is not a medical diagnosis, but a term people use to describe a temporary state of mental cloudiness. Think of dementia as the disease and Alzheimer's as one of the main causes. () It can feel like difficulty concentrating, forgetfulness, or struggling to find words. Brain fog can be triggered by stress, lack of sleep, hormonal changes, or illness, including the after Covid-19 effects or long fog is usually reversible but dementia is not since it is a brain-shrinking disease. Brain fog can mimic some symptoms of dementia, like confusion, forgetting instructions of something you're can also happen because of an illness or a side effect of medication or a symptom of an underlying condition, according to Cleveland fog feels like difficulty concentrating, confusion, fatigue, forgetfulness, losing your train of thought, mental exhaustion, not having the right words, slow thought process and reaction time and trouble paying attention. Brain fog is usually reversible but dementia is not since it is a brain-shrinking disease. () In some cases, many lifestyle factors can trigger brain fog, signalling brain tumors, which may initially manifest with subtle cognitive DIAGNOSIS CAN MAKE A DIFFERENCEWhile there is no cure for dementia or Alzheimer's, early diagnosis can help slow progression and improve quality of sooner it is detected, the better it can be managed, whether through medication, lifestyle changes, or caregiver signs like frequently forgetting appointments, misplacing objects, repeating questions, or withdrawing from social activities should not be ignored, especially if they worsen over brain fog, though milder, can still be disruptive. Treating it often means improving sleep, managing stress, and correcting nutritional deficiencies (vitamin D or B12). If it persists, a medical evaluation can rule out more serious Watch


Mint
5 hours ago
- Mint
Siddharth Pai: Can AI beat quantum computing at its own game?
For decades, quantum computing has been described as the 21st century's technological lodestar—with its unfathomable computational power poised to solve problems beyond the ken of classical machines. Quantum computers promise to crack cryptographic codes, simulate the quantum dynamics of molecules in material science, aid drug discovery and more. Yet, as the quantum race drags on, an unexpected challenger has emerged, not to dethrone but outpace it in precisely those domains where it was expected to shine the brightest: AI. To grasp the possibility of this disruption, begin with what quantum computing is. Unlike classical computers that encode information in binary bits—0s or 1s—quantum computers use quantum bits, or qubits, which can exist in a superposition of states. Also Read: Will AI ever grasp quantum mechanics? Don't bet on it Through entanglement and quantum interference, quantum computers can process a vast space of possibilities in parallel. This lets them model quantum systems naturally, making them ideal for simulating molecules, designing new materials and solving certain optimization problems. Among its most touted applications is its potential to transform material science. Advances with high-temperature superconductors, catalytic surfaces or novel semiconductors often require modelling the interactions of strongly correlated electrons—systems where the behaviour of one particle is tightly linked to that of many others. Classical algorithms falter in such simulations because the complexity of the quantum state space rises exponentially with system size. A full-fledged quantum computer would handle all this with ease. But the practical realization of quantum computing remains vexed. Qubits, whether superconducting loops, trapped ions or topological states, are exquisitely fragile. They 'decohere' (lose their quantum state) within microseconds and must be kept at temperatures colder than deep space. Error correction remains an uphill battle. Most of today's quantum machines can manage only a few hundred noisy qubits, far short of the millions needed for fault-tolerant computing. Also Read: Is Google's Willow really a 'wow' moment for quantum computers? In the meantime, artificial intelligence (AI), particularly deep learning, has made remarkable incursions into the same spaces. A turning point came in 2017 with a paper in Science by Giuseppe Carleo and Matthias Troyer ( Startling scientists, they found a neural network-based variational method to approximate the wave-function of quantum systems. This approach employed restricted Boltzmann machines to represent complex correlations among quantum particles, modelling the ground states of certain spin systems that had been hard to simulate classically. That paper didn't just introduce a new tool; it signalled a paradigm shift. Researchers used it for deep convolutional and autoregressive networks, transformer architectures and even diffusion models to simulate quantum many-body systems. These neural networks run on classical hardware and do not require the brittle infrastructure of quantum machines. It's not merely a question of catching up. AI is beginning to demonstrate capabilities in material discovery and quantum simulation that, while not perfectly accurate at the quantum level, are good enough. Generative models have proposed new crystalline structures with desirable thermal or electronic properties, while graph neural networks have predicted materials' phase behaviour without recourse to first-principle calculations. Most strikingly, AI models have begun to assist in inferring effective Hamiltonians—mathematical descriptions of physical systems—from experimental data, a tough task even for top-level experts. This acceleration has not gone unnoticed by major research labs. Google's DeepMind, for instance, has begun integrating machine learning tools directly into quantum chemistry workflows. Startups in the quantum space often include AI-based pre-processing or error mitigation in their pipelines. Also Read: Underdelivery: AI gadgets have been a let-down but needn't be A complementary field is fast becoming a competing one. AI will not make quantum computing irrelevant in the absolute sense, as there will always be quantum phenomena that only quantum devices can fully capture, but AI may take the lead in many practical problems before quantum hardware matures. If machine learning models can deliver 90% of the performance at 5% of the cost and infrastructure, industrial users may not wait for perfection. Moreover, there's a subtler factor at play: a shift in intellectual capital. The more investment AI-based methods attract, the more resources will flow into neural modelling over quantum error correction. By the time quantum machines mature, many of the use-cases originally envisioned for them may have been absorbed by AI tools that ironically use quantum data or theory. Quantum computing risks becoming a beautiful idea outpaced by a merely competent but deployable alternative. There is an irony here that would not be lost on Schrödinger or Feynman: that the classical world, once deemed too simplistic in the face of quantum reality, might be reasserting itself through the statistical abstractions of machine learning. We set out to build a machine that thinks like nature. Instead, we taught our machines to imitate nature well enough to move forward without grasping it fully. Quantum computing may still prove indispensable. But it will have to justify its place in a world where its promise is being appropriated by its upstart cousin AI. The author is co-founder of Siana Capital, a venture fund manager.