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Yahoo
23-05-2025
- Health
- Yahoo
What If a Selfie Could Predict Your Life Expectancy?
Imagine if a photo could tell you more about your health than your last check-up? A groundbreaking study from Mass General Brigham, published in journal The Lancet Digital Health, recently introduced FaceAge, an AI tool that estimates a person's biological age from a simple photograph. 'Doctors, myself included, still rely on the eyeball test—a split-second judgment of whether a patient looks robust or frail,' said Dr. Raymond Mak, a radiation oncologist and the director of clinical innovation for his department at the Dana-Farber Cancer Center, the faculty leader in AI implementation for the artificial intelligence in medicine (AIM) program at Mass General Brigham and an associate professor at Harvard Medical School, as well as a co-senior author of the study. More from Flow Space 3 Small Things This Orthopedic Surgeon Wishes Women Would Do for a Longer Life 'That snap impression is subjective, yet it influences treatment decision making every day,' he told Flow Space. So, researchers were curious just how well AI could help doctors diagnose. What they found was that in patients with cancer, looking biologically older than your chronological age, was linked with worse survival outcomes (on average, the FaceAge of cancer patients was about five years older than their chronological age). According to the study, FaceAge not only revealed aging patterns invisible to the naked eye but also outperformed doctors in predicting short-term life expectancy for patients receiving palliative care. 'Our goal was to improve that judgment from a subjective glance to a reproducible, data-driven metric by developing an artificial intelligence algorithm called FaceAge,' Mak explained. 'Such a tool gives doctors the ability to assess patient health at low-cost and repeatedly over time with just a simple face photograph.' An AI algorithm like FaceAge works by taking an image of a patient and then analyzing that image against a database of images of healthy individuals and those with cancer. Mak and his team recently expanded their datasets to include millions of healthy individuals and over 20,000 cancer patients to develop an even more accurate FaceAge algorithm and to test AI performance across a larger and more diverse group of patients. 'Also, we are doing some technical work to understand how the algorithm performs over different conditions including things like, varying skin tone, impact of cosmetic surgery, use of make-up or different lighting conditions and facial expression… like whether someone is smiling or sad,' he added. From there every image quantitatively produces a biological age estimation that is generated the same way every time, regardless of a clinician's experience level, fatigue or unconscious assumptions. 'Selfies for health!,' exclaimed Mak. 'When trained on a large and demographically varied set of face photos, the algorithm applies a consistent rule-set to every image, reducing the variability that creeps into one-to-one visual assessments,' he added. 'It does not replace the physician's judgement, but it does support that judgement with an objective reference point and flags when a patient's biological age appears discordant with their stated age.' While the study did have limitations and biases—with further validation in larger, ethnically diverse and younger cohorts necessary before clinical adoption—FaceAge offers valuable prognostic insights independent of conventional clinical factors, with statistically significant results, even after adjusting for chronological age, sex and cancer type. Mak added that doctors with access to FaceAge information have improved performance and reduced variability in predicting outcomes. 'By flagging people who are biologically older than their years, the technology could help us spot elevated risk for age-related conditions such as cancer and cardiovascular disease,' he said. For midlife women—who are most commonly diagnosed with breast, lung and gastrointestinal cancers—AI and FaceAge could have life changing implications. 'The new AI models are a different breed than the AI of the early 2000s, now with an ability to learn and evolve,' Dr. Katerina Dodelzon, a radiologist specializing in breast imaging and an associate professor of radiology at Weill Cornell Medicine, told Flow Space. 'New advances in AI include subsets termed machine learning, which is an AI that can learn to make predictions or decisions, and its subset of deep learning, which uses artificial neural networks. The more data a machine learning model is exposed to, the better it performs over time.' She says it can also help with: Earlier and More Accurate Detection Midlife women benefit greatly from early cancer detection, which improves survival rates for: : AI can detect subtle changes in mammograms up to two years earlier than radiologists. Lung Cancer: AI can flag early-stage nodules in low-dose CT scans. : AI-assisted colonoscopy improves adenoma detection rate. Personalized Treatment Plans AI helps oncologists tailor therapies based on a patient's unique profile: Genomic Data Analysis: AI can interpret massive genomic datasets to find actionable mutations. For example, in breast cancer, it helps identify candidates for hormone therapy, HER2-targeted therapy or immunotherapy, says Dodelzon. Treatment Optimization: AI evaluates past patient responses to suggest optimal chemotherapy regimens, dosage and predict side effects. Management Remote Monitoring Tools: Wearables and AI apps can track vital signs, symptoms and treatment side effects. This supports real-time intervention and minimizes doctor visits. AI Chatbots & Virtual Health Assistants: These can answer questions, schedule appointments and provide appointment and medication reminders. Equity and Access Many midlife women face healthcare disparities based on race, income or geography. And for women living in healthcare deserts, where access to care is limited, AI can: Improve Access to Expertise: AI tools bring expert-level diagnostic and treatment planning to underserved or rural areas via telehealth. Language and Literacy Support: AI-powered translation and plain-language medical explanations empower patients to understand and make informed choices. For Mak and his team harnessing AI to save more lives is the ultimate goal. They are currently developing new facial health recognition algorithms that can predict survival directly or other health conditions, in addition to conducting genetic analyses on a larger group of patients and opening two prospective studies. 'One is a clinical trial in cancer patients where we will compare FaceAge against conventional assessments of frailty in elderly patients,' said Mak. 'Second, we are about to open a healthy volunteer portal where people in the public can upload photos and get their own FaceAge estimate—and their photos will help us develop improved algorithms.' And the future of AI in healthcare is set to be transformative, shifting the industry from reactive to highly proactive, personalized and precise. Dodelzon says rather than replacing doctors, AI will augment their capabilities. This support will help catch conditions earlier, reduce diagnostic errors and streamline clinical decision-making. By leveraging vast datasets, AI will recommend treatment options tailored not only to clinical guidelines but also to a patient's unique biology and preferences. Moreover, AI will take over many of the time-consuming administrative tasks that burden healthcare professionals, such as documentation, billing and charting, which allows for more meaningful patient interaction and personalized care. 'I think the current advances and the future development and promise of these tools is very exciting, with the potential to augment many of the routine detection and characterization tasks, and even more exciting to me, the potential to provide more prognostic in addition to diagnostic information,' Dodelzon said. 'But that is what they are—'tools' in our 'doctor's bag' that allow us to do more for our patients.'
Time of India
21-05-2025
- Health
- Time of India
New AI tool can use selfies to predict how long a cancer patient will live
Christina Caron & Mohana Ravindranath It's no secret that some people appear to age faster than others, especially after enduring stressful periods. But some scientists think a person's physical appearance could reveal more about them than meets the eye — down to the health of their tissues and cells, a concept known as 'biological age'. In a new study, published in The Lancet Digital Health, researchers trained artificial intelligence to estimate the biological ages of adults with cancer by analysing photos of their faces. Study participants with younger estimates tended to fare better after treatment than those deemed older by AI, researchers at Mass General Brigham found. The findings suggest that people's biological age estimates are closely linked to their physical health, which could reflect their ability to survive certain treatments, the authors of the study said. Face-based aging tools have 'extraordinary potential' to help doctors quickly and inexpensively estimate how healthy their patients are, compared with existing tests, which use blood or saliva to measure chemical and molecular changes associated with aging, said William Mair, a professor of molecular metabolism at the Harvard TH Chan School of Public Health who was not involved in the study. FaceAge, the machine learning tool created by researchers at Mass General Brigham, found that study subjects with cancer appeared five years older than their chronological age. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Your Finger Shape Says a Lot About Your Personality, Read Now Tips and Tricks Undo The biological age of people without cancer was typically close to their actual age. And those who were categorised as older were more likely to die, either from cancer or other causes. The researchers are not the first to find a link between facial and biological aging: A study in Denmark found that subjects who looked older than their chronological age tended to die earlier than their twins. Doctors could one day use FaceAge to decide whether to provide different treatment depending on a patient's estimated biological age, said Dr Raymond H. Mak, a radiation oncologist at Mass General Brigham who worked on the study. Preliminary data suggests that FaceAge goes beyond the visual markers of age we might look to, like wrinkles, grey hair or baldness, and instead flags less obvious factors like hollowing of the temples (which reflects a loss of muscle mass) and the prominence of the skin folds on either side of the mouth, Dr Mak said. The authors of the study hope to eventually commercialise the technology and create a product that could be used in doctor's offices. They plan to file for a patent once the technology is more developed. The current version of the tool has limitations. It was primarily trained on white faces, Dr Mak said, so it could work differently for people with different skin tones. And it isn't clear to what extent modifications like plastic surgery, makeup, lighting or the angle of the face could affect the results. And while biological aging can be accelerated by a number of factors, like stress, pregnancy, smoking, drinking alcohol and even extreme heat, some of these changes can be reversible — and it's not clear if the tool would pick up those changes over time. Experts in medical ethics also have concerns. 'I'd be very worried about whether this tool works equally well for all populations, for example women, older adults, racial and ethnic minorities, those with various disabilities, pregnant women and the like,' said Jennifer E. Miller, the co-director of the program for biomedical ethics at Yale University. She and others in the field also wondered whether the tool might be used to justify denying insurance coverage or medical treatment. Dr Mak and other researchers who worked on the study have had reservations, too. 'We're really concerned about potential misuse of technology in general,' he said. However, he added, the researchers felt the tool would be more helpful than harmful — and it could be used to support, but not replace, clinicians' judgment. NYT News Service One step to a healthier you—join Times Health+ Yoga and feel the change
Time of India
15-05-2025
- Health
- Time of India
FaceAge AI tool can predict cancer survival from your selfie; here's how
FaceAge AI tool can predict cancer survival from your selfie; here's how Artificial intelligence (AI) is rapidly transforming healthcare, with new tools emerging that can assess patients' health in innovative ways. One of the latest advancements is FaceAge , an AI-driven system capable of estimating a person's biological age—a measure of how their body is aging—using just a facial photograph. Unlike chronological age, which simply counts the number of years a person has lived, biological age reflects the cumulative wear and tear on the body, influenced by genetics, lifestyle, and environment. This new approach could provide a more accurate assessment of overall health, potentially improving patient care and survival predictions, particularly for cancer patients. Recently published in The Lancet Digital Health, a groundbreaking study titled 'FaceAge, a deep learning system to estimate biological age from face photographs to improve prognostication' demonstrated that FaceAge can even predict cancer survival more accurately than many current clinical methods. What is FaceAge and how does it work FaceAge is a deep learning system developed by researchers at Harvard Medical School and other leading institutions. It uses advanced image processing and machine learning techniques to analyze facial photographs and estimate a person's biological age. Unlike traditional age estimation, which focuses on chronological years, FaceAge assesses the physiological signs of aging captured in facial features. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like AI guru Andrew Ng recommends: Read These 5 Books And Turn Your Life Around in 2025 Blinkist: Andrew Ng's Reading List Key features of FaceAge: Data-driven insights : Trained on a dataset of over 58,000 facial images from healthy individuals, and tested on 6,196 cancer patients across multiple hospitals in the United States and Europe. Biological age measurement : Goes beyond surface appearance, examining deep, structural changes in facial tissues and skin to assess biological aging. Genetic links : Identifies biological age markers linked to genes associated with cellular senescence and overall longevity, providing insights into aging at the molecular level. Advanced image analysis : Uses sophisticated algorithms to detect subtle facial changes that correspond to biological aging, such as skin elasticity, bone density, and muscle tone. How FaceAge predicts cancer survival One of the most significant findings in the recent study is that FaceAge can predict cancer survival more accurately than doctors relying on traditional metrics like chronological age. The study divided cancer patients into three major groups: Curative patients : Those receiving potentially curative treatments, primarily radiation therapy. Thoracic cancer patients : Those with cancers affecting the chest, including lung and esophageal cancers. Palliative patients : Those with advanced-stage or metastatic cancers, often in palliative care settings. In all three groups, FaceAge consistently outperformed traditional age in predicting survival outcomes. Notably, it was able to detect the accelerated aging associated with cancer progression, which often goes unnoticed by conventional assessments. Key findings: Curative group : Higher FaceAge scores were linked to significantly lower survival rates, suggesting that patients who appear biologically older are less likely to survive despite aggressive treatment. Thoracic group : FaceAge provided more accurate survival predictions, even when doctors had full clinical data, highlighting the tool's potential for personalized cancer care . Palliative group : In patients receiving end-of-life care, FaceAge improved survival predictions when integrated with established clinical tools like the TEACCH model, allowing for better care planning. Why biological age matters in cancer prognosis Chronological age often fails to capture the true health status of a patient. Two people of the same age can have vastly different biological ages depending on lifestyle, genetics, and medical history. This discrepancy is particularly relevant in cancer care, where treatment decisions must balance the potential benefits against the physical toll on a patient. Key advantages of biological age measurement: Personalised treatment : Allows for more tailored treatment plans based on a patient's actual biological condition, not just their birth date. Improved survival predictions : Identifies patients at higher risk of poor outcomes, enabling more proactive interventions. Reduced treatment risks : Helps avoid overtreatment in biologically older patients who may not tolerate aggressive therapies. Potential impact on healthcare and cancer treatment FaceAge has the potential to revolutionise how oncologists assess patient fitness for treatment. By providing a clearer picture of biological aging, it could lead to more precise and personalized treatment strategies. However, the technology is still in its early stages and will require further testing across diverse populations before it can be widely adopted. Future applications: Clinical trials : Could be used to stratify patients more effectively, improving the quality of clinical research. Remote health monitoring : Offers a non-invasive, image-based method for ongoing health assessments. Elderly care : May help in assessing frailty and fall risk in older adults. Ethical and privacy concerns Despite its potential, FaceAge raises several ethical concerns. Using facial images to assess health introduces privacy risks, as this data could be misused by employers, insurers, or even governments. Researchers have also warned that the AI model might produce biased results if not properly calibrated for different races, genders, and age groups. Challenges and considerations: Data privacy : Protecting sensitive medical and facial data from misuse. Algorithmic fairness : Ensuring the model performs accurately across diverse populations. Regulatory oversight : Developing clear guidelines to prevent misuse and ensure transparency. AI Masterclass for Students. Upskill Young Ones Today!– Join Now
Medscape
15-05-2025
- Health
- Medscape
Obesity App May Enable Weight Loss With Less Semaglutide
MÁLAGA, Spain — An online obesity clinic named Embla was associated with weight loss like that seen in randomized controlled trials (RCTs) of semaglutide, but with less than half of the semaglutide dose, according to new real-world data. Average weight loss reached 16.7% after 64 weeks' use of the digital health platform, which combines tailored semaglutide dosing with intensive behavioral therapy delivered via an app. In clinical trials of semaglutide, the dose was 2.4 mg/wk, but patients in the present study used an average of 1 mg/wk. Similar mean weight loss was observed across baseline body mass index (BMI) classes and with all medication doses. 'This [study] showed us that combining an intensive behavioral intervention within an e-health weight management clinic can reproduce some of the results we see in confirmatory trials but in the context of a real-world cohort and with far less medication used,' said lead researcher Henrik Gudbergsen, MD, chief medical officer at Embla and general practitioner. 'The intensive behavioral therapy is key, and it goes a long way when you want to lose weight,' Gudbergsen added. He presented the findings at European Congress on Obesity (ECO) 2025. Publication of the study in The Lancet Digital Health is pending, but a preprint version is available. App Plus Semaglutide Real-world data on semaglutide use in digital weight-loss interventions remain limited. Studies in this setting often show less weight loss than in RCTs of semaglutide and rarely report the details of lifestyle interventions or dosing patterns, said Gudbergsen. Real-world data also suggest that few patients follow the dose titration used in clinical trials, and most stay on lower doses than the standard 2.4-mg once-weekly dose, he added. This study of the Embla platform evaluated the effects of combining video and chat-based coaching with diet, exercise, and psychological support using cognitive-behavioral therapy and acceptance and commitment therapy. Embla users interact with a multidisciplinary team, including doctors, nurses, and psychologists. The artificial intelligence–powered app also tracks weight, medication, physical activity, and patient-reported outcomes. Semaglutide was prescribed to participants using a personalized, treat-to-target approach, titrated based on tolerability. The primary endpoint was percentage weight change at 64 weeks. The average weekly semaglutide dose was a key secondary outcome. A total of 2694 participants with a BMI ≥ 27 were enrolled. At 64 weeks, 465 participants remained in the study. 'Although this is a large dropout [rate] after 1 year in more conventional RCT terms, it is actually very good for a real-world intervention,' Gudbergsen said in response to an audience comment by practitioner and semaglutide drug trialist Donna Ryan, MD, professor emerita at Pennington Biomedical Research Center in Baton Rouge, Louisiana. Behavioral Therapy Mean weight loss after 64 weeks was 16.7% (95% CI, 17.4%-16.0%) with an average semaglutide dose of 1.02 mg/wk (SD, 0.56). Women had 17.6% (95% CI, 18.4%-16.9%) and men had 13.4% (95% CI, 14.3%-12.5%) weight loss. The app captured engagement metrics, and Gudbergsen noted that women tended to interact with it more frequently. 'We do see a higher engagement among women and a tendency for women to engage more in communities. This might explain some of the differences between the cohorts,' he pointed out. Weight loss was approximately similar across baseline BMI categories for the first 6 months. Participants with a BMI of 27-29.9 lost 10.8% (95% CI, 12.7%-8.8%), those with a BMI of 30-34.9 lost 15.9% (95% CI, 16.6%-15.1%), those with a BMI of 35-39.9 lost 17.5% (95% CI, 18.2%-16.7%), and those with a BMI of ≥ 40 lost 17.2% (95% CI, 18.2%-16.2%). 'Then there's a split, with people with a baseline BMI between 27 and 30 losing slightly less than those with a BMI above 30,' said Gudbergsen. Weight-loss outcomes remained consistent across semaglutide dose levels. For a dose of 0.25 mg/wk, weight loss was 17.3% (95% CI, 18.5%-16.0%); for 0.5 mg/wk, 17.3% (95% CI, 18.4%-16.2%); for 0.75 mg/wk, 18.1% (95% CI, 19.4%-16.7%); and for 1 mg/wk, 17.6% (95% CI, 18.6%-16.5%). Weight loss of at least 5% and 10% was achieved by 98% and 82% of participants, respectively, over the 64 weeks. About 29% of participants who reached 64 weeks titrated above the 1-mg semaglutide dose. When asked about adverse event reporting, Gudbergsen responded, 'There's spontaneous reporting, but in addition, the healthcare professionals ask patients if they've experienced adverse events.' According to the preprint, depending on the severity of the adverse event report, the participant could either be 'maintained at current dose, reduced to a dose in between standard doses, reduced to the previous titration dose, or terminated until the adverse event was properly handled.' Support Is Key Commenting on the study for Medscape Medical News , Jason Halford, PhD, head of the School of Psychology at the University of Leeds, Leeds, England, and past president of the European Association for the Study of Obesity, said, 'It shows an important element of weight management: That is, the support. 'If you've got an app, and there's somebody to provide help with behavioral challenges through interacting with the app, then that's providing the support we need,' Halford added. 'These drugs are not meant to be given on their own. This [study] shows the benefit of additional obesity support mechanisms or tools we can bring to bear,' he said. 'Not only do we find people lose similar amounts of weight on a lower amount of drug, but it might also protect them when they come off the drug because there's lasting change. If you don't get the behavior change in while you're on the drug, once the drug is not there helping you, you're set up for failure.' Embla costs users €150 per month to start, while patients can shift to a maintenance program once appropriate.
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Business Standard
13-05-2025
- Health
- Business Standard
AI tool claims your selfie can predict cancer survival more accurately
What if your face could reveal more about your health than your actual age? Scientists have developed a new artificial intelligence (AI) tool called FaceAge, which uses just a photo of your face to estimate your biological age—an indicator of how your body is ageing. Published recently in The Lancet Digital Health, the study titled ' FaceAge, a deep learning system to estimate biological age from face photographs to improve prognostication ' found that FaceAge not only measures ageing more precisely than a birthday count but also predicts survival chances in cancer patients better than doctors do. What is FaceAge and how does it work? FaceAge is a deep learning system developed by researchers at Harvard Medical School and partners. It analyses simple face photographs to predict a person's biological age, which can be different from their chronological age. Unlike a traditional age number, biological age reflects the wear-and-tear on your body due to genetics, lifestyle, disease, and environment. The AI model uses face detection and feature extraction techniques to estimate how old someone looks biologically. It was trained on over 58,000 face images of healthy people and tested on 6,196 cancer patients across multiple hospitals in the US and Europe. FaceAge predicts cancer survival more accurately than doctors According to the paper, in three major cancer patient groups—curative (patients receiving curative treatments, primarily radiation therapy), thoracic (patients with cancers in the chest area), and palliative (patients with advanced-stage cancer)—FaceAge was a stronger predictor of survival than chronological age, even when doctors already had full clinical information. In patients receiving curative cancer treatments, higher FaceAge scores correlated with significantly worse survival outcomes. For those with metastatic cancer receiving palliative care, FaceAge improved survival predictions when added to existing clinical tools, like the TEACCH (Treatment and Education of Autistic and Communication-related handicapped CHildren) model. Why biological age is more useful than chronological age Chronological age doesn't always match how healthy or frail someone actually is. For instance, a 65-year-old who exercises regularly and eats well might have the body of a much younger person, while a 28-year-old who smokes and eats poorly might age faster internally. FaceAge fills this gap by identifying biological ageing signs invisible to the naked eye. In this study, cancer patients looked nearly five years older than their real age, on average. This facial age difference was linked to poorer survival outcomes, making FaceAge a potential game-changer in clinical settings. FaceAge was also found to be genetically associated with senescence genes—linked to the process of cellular senescence—which plays a role in ageing and cancer development. Chronological age showed no such correlation. FaceAge could transform cancer treatment—but not yet Researchers believe FaceAge could help doctors make more objective treatment decisions, particularly in cancer care, where risks and benefits must be weighed carefully. It could also support personalised medicine and clinical trials by offering a consistent measure of patient health. Ethical concerns around AI and facial health prediction The use of AI tools like FaceAge raises ethical and privacy concerns. Researchers warn of potential misuse by insurance firms or employers to make decisions about individuals based on facial data. There's also uncertainty about whether FaceAge performs equally across races, age groups, and genders—or if results could be skewed by facial features, cosmetic surgeries, or image quality. The authors call for strong regulation and additional research to ensure AI tools in healthcare are fair, transparent, and safe.
