Research
The Centre for Digital Health and Precision Medicine aims to leverage the extensive longitudinal patient database of the Apollo Hospitals Group, and datasets of the University of Leicester and other Consortium Partners to deliver improved population health with a global perspective through better disease prediction and prevention.
The Centre will conduct high-quality research in any areas of clinical medicine where advanced analytics leading to digital health or precision medicine products can improve health or its delivery. Our work is dedicated to making a tangible difference in patient lives and healthcare systems worldwide, leading to improved and earlier detection, diagnosis and management of multiple acute and long-term conditions in hospital and community settings.
Areas of focus
Ankle Fracture
Uterine Cancer (Oncology)
Mesothelioma (Oncology)
Glioblastoma
Polygenic Risk Score
ALIVE-EWS
Metabolic Diseases
Mammography
Liver Fibrosis
ADPKD
Ambient Listening in OR
Ankle Fracture
Ankle fractures are among the most common musculoskeletal injuries requiring emergency care. Our research applies computer vision and deep learning techniques to standard digital radiographs to automate fracture detection, classify fracture severity, and support emergency clinicians in fast-tracking orthopedic triage and treatment planning.
Faculty Members
Prof. Jitendra Mangwani (Leicester) & Prof. Raju Vaishya (Apollo)
Uterine Cancer (Oncology)
Uterine (endometrial) cancer incidence is rising, requiring refined stratification to optimize surgical and adjuvant therapies. By developing AI models that combine histopathological features, genomic biomarkers, and pelvic imaging, this project aims to predict recurrence risk and guide patient-tailored, fertility-sparing or aggressive treatment regimens.
Faculty Members
Prof. Esther Moss (Leicester) & Prof. Roma Sinha (Apollo)
Mesothelioma (Oncology)
Mesothelioma is an aggressive and typically late-diagnosed asbestos-related malignancy. We are building AI tools to analyze low-dose CT scans and occupational exposure registries to identify early pleural abnormalities and biomarker trends. Early detection aims to enable curative surgery or early systemic therapy, drastically improving long-term patient survival.
Faculty Members
Prof. Dean Fennell (Leicester) & Prof. Rakesh Jalali (Apollo)
Glioblastoma
Glioblastoma is an aggressive brain cancer with complex tumor heterogeneity and challenging clinical management. We utilize advanced medical image computing, genomic profiling, and multimodal AI to analyze tumor margins, predict recurrence patterns, and personalize radiotherapeutic and immunotherapeutic plans for patients.
Faculty Members
Dr. Spyridon Bakas
Polygenic Risk Score
Polygenic Risk Scores (PRS) aggregate the effects of thousands of genetic variants to estimate an individual's genetic predisposition to complex diseases. Our research focuses on developing and validating ancestry-specific PRS models, integrating them with clinical and lifestyle biomarkers, and implementing them into clinical workflows to enable personalized risk stratification, early disease screening, and tailored preventive interventions.
Faculty Members
Prof. Nilesh Samani (Leicester) & Dr. Shree Vidya (Apollo)
ALIVE-EWS
The ALIVE Early Warning System (EWS) is a real-time clinical decision support tool designed to detect early physiological signs of patient deterioration. By integrating continuous multi-parameter vital signs monitoring with predictive machine learning algorithms, the system aims to alert care teams ahead of critical events, improving patient safety and outcomes in acute care settings.
Faculty Members
Prof. Tim Coats (Leicester) & Dr. Sai Praveen (Apollo)
Metabolic Diseases
Chronic metabolic disorders, including type 2 diabetes, obesity, and dyslipidemia, present growing global health crises. Our research focuses on precision medicine strategies that combine genomic, metabolomic, and continuous clinical tracking data to understand individual disease trajectories, predict therapeutic responses, and enable tailored lifestyle and pharmacological interventions.
Faculty Members
Prof. Kamalesh Khunti (Leicester)
Mammography
Breast cancer remains a leading cause of cancer-related mortality globally. This project develops and evaluates deep learning architectures to assist radiologists in mammography screening. By improving the sensitivity of lesion detection, reducing false-positive recall rates, and identifying subtle microcalcifications, we strive to make screening more accurate and accessible.
Faculty Members
Prof. David Adlam (Leicester)
Liver Fibrosis
Liver fibrosis is a silent progression of chronic liver injury characterized by the excessive accumulation of extracellular matrix proteins. Our research aims to leverage AI-driven predictive modeling, multimodal data integration, and non-invasive digital biomarkers to detect early-stage fibrosis, predict progression risks, and design personalized management strategies that avoid progression to irreversible cirrhosis.
Faculty Members
Dr. Sudarshan Dadar (Apollo)
ADPKD
Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a progressive genetic disorder causing multiple fluid-filled cysts to enlarge the kidneys, leading to potential renal failure. Our research leverages longitudinal clinical data and AI-driven imaging analytics to track cyst volume, predict disease progression rates, and develop non-invasive prognostic markers to guide early therapeutic intervention.
Faculty Members
Dr. Sanjay Maitra (Apollo)
Ambient Listening in OR
The operating room is a high-stakes, fast-paced environment where accurate documentation is critical but time-consuming. This project develops ambient listening and natural language processing models tailored for the surgical environment. By capturing and structuring real-time verbal communication and surgical steps, the system aims to automate clinical documentation, reduce administrative burden on surgical teams, and enhance patient safety.
Faculty Members
Dr. Gang (Leicester)
Ankle Fracture
Ankle fractures are among the most common musculoskeletal injuries requiring emergency care. Our research applies computer vision and deep learning techniques to standard digital radiographs to automate fracture detection, classify fracture severity, and support emergency clinicians in fast-tracking orthopedic triage and treatment planning.
Faculty Members
Prof. Jitendra Mangwani (Leicester) & Prof. Raju Vaishya (Apollo)
Uterine Cancer (Oncology)
Uterine (endometrial) cancer incidence is rising, requiring refined stratification to optimize surgical and adjuvant therapies. By developing AI models that combine histopathological features, genomic biomarkers, and pelvic imaging, this project aims to predict recurrence risk and guide patient-tailored, fertility-sparing or aggressive treatment regimens.
Faculty Members
Prof. Esther Moss (Leicester) & Prof. Roma Sinha (Apollo)
Mesothelioma (Oncology)
Mesothelioma is an aggressive and typically late-diagnosed asbestos-related malignancy. We are building AI tools to analyze low-dose CT scans and occupational exposure registries to identify early pleural abnormalities and biomarker trends. Early detection aims to enable curative surgery or early systemic therapy, drastically improving long-term patient survival.
Faculty Members
Prof. Dean Fennell (Leicester) & Prof. Rakesh Jalali (Apollo)
Glioblastoma
Glioblastoma is an aggressive brain cancer with complex tumor heterogeneity and challenging clinical management. We utilize advanced medical image computing, genomic profiling, and multimodal AI to analyze tumor margins, predict recurrence patterns, and personalize radiotherapeutic and immunotherapeutic plans for patients.
Faculty Members
Dr. Spyridon Bakas
Polygenic Risk Score
Polygenic Risk Scores (PRS) aggregate the effects of thousands of genetic variants to estimate an individual's genetic predisposition to complex diseases. Our research focuses on developing and validating ancestry-specific PRS models, integrating them with clinical and lifestyle biomarkers, and implementing them into clinical workflows to enable personalized risk stratification, early disease screening, and tailored preventive interventions.
Faculty Members
Prof. Nilesh Samani (Leicester) & Dr. Shree Vidya (Apollo)
ALIVE-EWS
The ALIVE Early Warning System (EWS) is a real-time clinical decision support tool designed to detect early physiological signs of patient deterioration. By integrating continuous multi-parameter vital signs monitoring with predictive machine learning algorithms, the system aims to alert care teams ahead of critical events, improving patient safety and outcomes in acute care settings.
Faculty Members
Prof. Tim Coats (Leicester) & Dr. Sai Praveen (Apollo)
Metabolic Diseases
Chronic metabolic disorders, including type 2 diabetes, obesity, and dyslipidemia, present growing global health crises. Our research focuses on precision medicine strategies that combine genomic, metabolomic, and continuous clinical tracking data to understand individual disease trajectories, predict therapeutic responses, and enable tailored lifestyle and pharmacological interventions.
Faculty Members
Prof. Kamalesh Khunti (Leicester)
Mammography
Breast cancer remains a leading cause of cancer-related mortality globally. This project develops and evaluates deep learning architectures to assist radiologists in mammography screening. By improving the sensitivity of lesion detection, reducing false-positive recall rates, and identifying subtle microcalcifications, we strive to make screening more accurate and accessible.
Faculty Members
Prof. David Adlam (Leicester)
Liver Fibrosis
Liver fibrosis is a silent progression of chronic liver injury characterized by the excessive accumulation of extracellular matrix proteins. Our research aims to leverage AI-driven predictive modeling, multimodal data integration, and non-invasive digital biomarkers to detect early-stage fibrosis, predict progression risks, and design personalized management strategies that avoid progression to irreversible cirrhosis.
Faculty Members
Dr. Sudarshan Dadar (Apollo)
ADPKD
Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a progressive genetic disorder causing multiple fluid-filled cysts to enlarge the kidneys, leading to potential renal failure. Our research leverages longitudinal clinical data and AI-driven imaging analytics to track cyst volume, predict disease progression rates, and develop non-invasive prognostic markers to guide early therapeutic intervention.
Faculty Members
Dr. Sanjay Maitra (Apollo)
Ambient Listening in OR
The operating room is a high-stakes, fast-paced environment where accurate documentation is critical but time-consuming. This project develops ambient listening and natural language processing models tailored for the surgical environment. By capturing and structuring real-time verbal communication and surgical steps, the system aims to automate clinical documentation, reduce administrative burden on surgical teams, and enhance patient safety.
Faculty Members
Dr. Gang (Leicester)
Concept Proposal
We are interested in working with you, If you would like to submit a proposal for consideration, please use the form below.