This technology is a machine learning tool that improves the diagnosis of cutaneous T-cell lymphoma (CTCL) by integrating the analysis of genetic markers with patient electronic health records.
Cutaneous T-cell lymphoma (CTCL) is a rare skin cancer that is often misdiagnosed for other skin disorders. The mechanisms underlying CTCL development are poorly understood, which complicates its diagnosis and treatment. Early detection, diagnosis, and treatment of CTCL are critical in providing care for patients, particularly those with more aggressive forms of the disease. Identifying genetic markers and mutations associated with CTCL could help ensure a faster and more accurate diagnosis, guide treatment decisions, and differentiate between early and more advanced stages of the disease. This would improve patient stratification for both treatment and prognosis.
This technology is a machine learning and artificial intelligence tool that improves the diagnosis of cutaneous T-cell lymphoma (CTCL). Specifically, the technology identifies gene mutations associated with disease survival, prognosis, and severity and uses natural language processing to analyze electronic health records, linking pre-diagnosis criteria with subsequent CTCL diagnosis. By integrating multimodal patient data, this technology improves predictions related to disease development, survival, treatment response, and clinical outcomes. As a result, this approach can aid in timely and accurate CTCL diagnosis and help stratify patients into appropriate treatment groups depending on disease severity and genetic profile.
This technology has been validated using human patient data and electronic health records.
IR CU24366
Licensing Contact: Cynthia Lang
