AI Research
USF-developed facial analysis AI detects PTSD in kids • St Pete Catalyst
Diagnosing post-traumatic stress disorder (PTSD) in children presents significant challenges, as many struggle to share their experiences or explain how they feel. Artificial intelligence could help clinicians overcome those hurdles.
Researchers at the University of South Florida have combined their expertise in childhood trauma and artificial intelligence (AI) to create an objective, cost-effective tool that helps identify PTSD through facial expressions. The technology also tracks recovery, as the overarching goal is to improve pediatric and adolescent patient outcomes.
Alison Salloum, a professor at the USF School of Social Work, and Shaun Canavan, an associate professor in the Bellini College for Artificial Intelligence, Cybersecurity and Computing, lead an interdisciplinary team developing the system. They found that AI can detect distinct patterns in the facial movements of youth who have experienced trauma.
“Avoidance is a main component of PTSD, so children don’t want to talk about it,” Salloum said. “There are lots of reasons – one is just the sheer horror of what happened.”
Clinicians typically rely on subjective clinical interviews and self-reported questionnaires when diagnosing PTSD in children. Cognitive development, language skills, avoidance behaviors and emotional suppression often hinder those efforts.
Salloum also noted that children are reluctant to verbalize their experiences “because they don’t want to upset their parents any more.” Many children realize that revisiting traumatic experiences will compound the emotional toll on their parents and instead choose to compartmentalize their thoughts.
However, Salloum noticed that “child after child” exhibited intense facial expressions during virtual interviews for a clinical trial. She asked Canavan if he could systematically capture those moments to help them understand “what children are going through.”
Canavan, who specializes in facial analysis and emotion recognition, was happy to provide his technological expertise. He repurposed existing lab tools to build a new system that prioritizes patient privacy.
“I was confident it would work,” Canavan said.
Their study, published in Science Direct, validated his self-assurance. The first step was ensuring anonymity.
Canavan stressed that they didn’t use “raw features,” and only kept unidentifiable data on facial movements, head poses and whether the child was talking to a parent or clinician. Salloum called that a “critical piece” of the system that will foster future use.
Their study is now the first to incorporate contextual PTSD classifications while preserving data privacy. The interdisciplinary team built the model after recording 18 sessions with children as they shared traumatic experiences.
Canavan’s AI had over 100 minutes of video per child, each containing roughly 185,000 frames, to extract subtle facial movements linked to emotional expression. The technology detected distinct patterns, including the inability to convey emotion, among those with PTSD.
Salloum explained that accurately understanding symptomology will lead to better treatments, help track patient progress and determine when care should conclude. “We also want to make sure that when we’re ending treatment, that child is really back on track developmentally and not experiencing post-traumatic reactions,” she said.
“It was not surprising that our hypothesis, the way we set it up, worked,” Canavan said. “We’ve had previous experiments in other areas where we’ve shown this analysis works.”
He said AI is “absolutely another tool” for health care providers. The team now hopes to refine their system.
Rather than waiting to process videos, Canavan wants to create a model that analyzes facial expressions in real-time. His team would then create a user interface for clinicians that provides an immediate analysis.
Salloum said adolescents were eager to volunteer for the pilot study. “They liked the idea of technology helping.”
However, with additional funding, she hopes to test the AI on pediatric patients hindered by their cognitive development. “For young children, we have to rely on parent assessment and interviewing the parent,” she added.
Canavan said the system could benefit adults, like combat veterans and domestic abuse survivors, who internalize PTSD symptoms. A “big part” of his research revolves around applying technology that works for one group of people to different demographics.
Canavan stressed that AI is a clinical aid rather than a substitute. Salloum noted the importance of ensuring a model is valid and accurate “before people become comfortable with it.”
“It doesn’t replace the other methods of asking questions, conversations and interviews about what the person has experienced,” she continued. “It’s really a tool.”
AI Research
Hong Kong start-up IntelliGen AI aims to challenge Google DeepMind in drug discovery
In an interview with the Post, founder and president Ronald Sun expressed confidence that IntelliGen AI could soon compete globally with Isomorphic Labs, a spin-off of DeepMind, in leveraging AI for drug screening and design.
“For generative science, new breakthroughs and application opportunities are global in nature,” Sun said. “Within 12 to 18 months, we aim to land major, high-value clients on a par with Isomorphic.”
The term “generative science”, although not widely recognised yet, refers to the use of AI to model the natural world and facilitate scientific discovery.
The company’s ambitious plan follows the launch of its IntFold foundational model, which is designed to predict the three-dimensional structures of biomolecules, including proteins. The model’s accuracy levels were comparable to DeepMind’s AlphaFold 3, according to IntelliGen AI.
AI Research
Hong Kong start-up IntelliGen AI aims to challenge Google DeepMind in drug discovery
In an interview with the Post, founder and president Ronald Sun expressed confidence that IntelliGen AI could soon compete globally with Isomorphic Labs, a spin-off of DeepMind, in leveraging AI for drug screening and design.
“For generative science, new breakthroughs and application opportunities are global in nature,” Sun said. “Within 12 to 18 months, we aim to land major, high-value clients on a par with Isomorphic.”
The term “generative science”, although not widely recognised yet, refers to the use of AI to model the natural world and facilitate scientific discovery.
The company’s ambitious plan follows the launch of its IntFold foundational model, which is designed to predict the three-dimensional structures of biomolecules, including proteins. The model’s accuracy levels were comparable to DeepMind’s AlphaFold 3, according to IntelliGen AI.
AI Research
Ireckonu’s AI Research Revolutionizes Hospitality with Timely Churn Prevention Strategies
Thursday, July 10, 2025
Dr. Rik van Leeuwen, Head of Data Solutions and Customer Success at Ireckonu, has just uncovered the hospitality industry’s first ever methodology for customer churn management.
The historic study, conducted in collaboration with one of the top North American chains, uses artificial intelligence (AI) to determine the very moment the guest is most likely to drift away—and how hotels can intervene to prevent them from doing just that.
The study outcomes confirm that predictive models powered by artificial intelligence are able to effectively calculate the risk of a guest exiting, allowing hotel managers to take action at the point of the moment. The proactive action might involve the issuance of a discount offer, for instance the issuance of the 20% discount email, once the guest has reached 75% churn risk.
These last-moment offers greatly favor the chances of rebooking, hence enhancing the general guest retention figures.
This breakthrough is the result of the culmination of a multi-week research project combining artificial intelligence, machine learning, and advanced data modeling techniques to yield usable insights for hospitality operations. The framework is focused on predicting customer behavior, identifying risk of churn, and providing tailored recommendations for optimizing retention efforts.
The Power of Hospitality Through the Assistance of AI: Evolution from Forecast to Execution
It’s not just about identifying at-risk guests, but more about intervening at the right time. Dr. van Leeuwen identified the importance of not just knowing who is at risk, but knowing the time and how to intervene. “It’s no longer enough to know who’s at risk.
The value is in knowing how and when to react,” added Dr. van Leeuwen. “That’s where hospitality strategy gains the promise of AI.”
The Dr. van Leeuwen system combines the BG/NBD (Beta-Geometric/Negative Binomial Distribution) model for churn probability with reinforcement learning for future engagement.
The BG/NBD model, in general use for subscription and non-subscription companies, anticipates the probability of repeat purchasing by the customer in the future. By including reinforcement learning, the model by Ireckonu doesn’t just anticipate churn by the customer, but determines the best actions to take, allowing for in-the-moment adjustment based on evolving guest behavior.
Unlike traditional “black box” type artificial intelligence systems, in which interpretability is difficult and implementation in routine business environments is complex, Dr. van Leeuwen’s approach emphasizes transparency and flexibility. The model is designed to be a “white-box” system in the sense that managers in the hotels will understand and rely on the system recommendations based on the used data.
Transparency in this context is the driving factor behind adoption in the hospitality industry, where operating decisions have to be efficient and implementable.
From the Lab to the Field: The Practicality of Ireckonu’s Solutions
Ireckonu has already started integrating these learnings into its broader middleware and customer data platform offerings, allowing hotel chains and other hospitality offerings to deploy AI-drived guest retention approaches at the point of operation. The platform integrates seamlessly with existing hotel management systems in operation, allowing businesses to deploy immediate, data-driven action whenever the system recognizes a guest as being at risk.
“We’re not just pushing academic theory” said CEO of Ireckonu, Jan Jaap van Roon. “Rik’s research brings scientific validation to one of the areas where hotels have long underperformed: guest loyalty. That’s not theory—it’s proven, practical insight. And it’s the kind of innovation we promote at Ireckonu.”
The study’s results have universal applicability to the hotel industry and beyond. The company is exploring how to further optimize the model by incorporating additional variables, such as sentiment and dynamically changing the price in response to the customer’s specific churn risk in coming developments of its AI-powered solutions.
Prospects for Future Development and Applications
For the future, Dr. van Leeuwen’s research opens promising opportunities for further refinements to the artificial intelligence model. One potential area in the future where one might realize developments is in the integration of qualitative guest commentary, e.g., customer review sentiment analysis, to the churn forecasting model. By considering not only the quantitative measures but the emotional and experience facets of the guest’s experience, the model would have the potential for even more accuracy in recommendations for retention efforts.
Furthermore, the AI framework developed by the study has the possibility of being extended beyond the hotel industry. Other areas where high frequency, non-contractual customer interactions occur, i.e., retail and services, would be able to utilize corresponding churn prediction models to maximize customer interaction and retention efforts.
Ireckonu’s ongoing investment in research and development is evidence of its dedication to delivering the hospitality business more intelligent, more tailored guest experiences. By utilizing clean, actionable guest data, the company is helping hotels make more effective retention activities and in the end offer the customer service they desire.
Conclusion:
The Future of Hospitality is AI-Driven As the hotel industry becomes increasingly dependent on artificial intelligence and data science to maximize guest retention, Ireckonu’s research sets the standard in churn management. Identifying the exact moment the guest is most likely to disengage, and providing hotels with concrete action steps, Ireckonu is rethinking the way hospitality businesses approach guest loyalty. This breakthrough shines the spotlight not only on the promise of artificial intelligence for the hospitality profession, but also the worth of marrying cutting-edge research with in-the-field application.
The hospitality future will be guided by more informed, data-driven decisions—decisions that maximize the guest experience, enhance guest loyalty, and achieve long-term business success.
Tags: AI hospitality churn, BG/NBD model, Canada, churn risk, customer behavior prediction, customer retention, Dr. Rik van Leeuwen, guest engagement, guest loyalty, hospitality sector AI solutions, hotel guest disengagement, hotel industry technology, Ireckonu, north america, predictive AI, reinforcement learning, usa
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