Construction of a Psoriasis and Psoriatic Arthritis Diagnostic Model Based on Multidimensional Nail Information

Psoriasis is a globally prevalent chronic relapsing skin disease, characterized by its long duration and tendency to relapse. In addition to skin symptoms, it can also affect nails and joints, leading to pathological features such as pitting, leukonychia, red lunula, or severe nail dystrophy. Some patients with psoriasis may develop psoriatic arthritis. Psoriatic arthritis (PsA) is a chronic relapsing musculoskeletal disease, characterized by psoriatic skin lesions accompanied by axial and peripheral joint damage, and often associated with characteristic manifestations of psoriatic nails. These nail changes typically indicate more severe disease and poorer prognosis. However, current diagnostic methods largely depend on the experience and professional knowledge of clinicians, which are subjective and uncertain. Moreover, histopathological examination is invasive and can cause additional pain and inconvenience to patients. To develop an effective, convenient, and non-invasive early diagnostic tool for psoriasis, our research team has conducted in-depth studies in the field of psoriasis-related diagnosis and predictive models. We have successfully developed a predictive model for psoriatic arthritis, including six key predictive factors: history of joint swelling, history of arthritis, history of swelling and pain in fingers or toes, nail involvement, genital involvement, and a history of long-term local use of corticosteroids. Clinicians can effectively assess the risk of psoriatic arthritis by obtaining information about these six factors from patients. The paper "Early detection of psoriatic arthritis in patients with psoriasis: construction of a multifactorial prediction model" was published in Front. Immunol (DOI: 10.3389/fimmu.2024.1426127). Raman spectroscopy is a rapid, non-invasive molecular vibration detection method that has shown great potential in medical diagnostics. Studies have shown that Raman spectroscopy can distinguish normal and abnormal tissues