Pilot study for the clinical validation of an artificial intelligence algorithm to optimize the appropriateness of dermatology referrals. (LEGIT.HEALTH_DAO_Derivation_O_2022)

Study type

• Study topic:
  Disease /health condition

  Medical device
• Study type:
  Non-interventional study
• Scope of the study:
  Effectiveness study (incl. comparative)

  Feasibility analysis

  Healthcare resource utilisation
• Data collection methods:
  Combined primary data collection and secondary use of data

Non-interventional study

• Non-interventional study design:
  Cohort

  Cross-sectional

Population studied

• Short description of the study population:
  Adult patients (≥ 18 years) with skin pathologies seen in the primary care service of health centers referring to Cruces and Basurto University Hospitals.
• Age groups:
  Adults (18 to < 46 years)

  Adults (46 to < 65 years)

  Adults (65 to < 75 years)

  Adults (75 to < 85 years)

  Adults (85 years and over)
• Estimated number of subjects:
  400

Study design details

• Study design:
  This is a prospective observational analytical study of a longitudinal clinical case series. The study does not involve an active or control group, as it is focused on the evaluation of the device in a real-world clinical setting.
• Main study objective:
  To validate that the device developed by Legit.Health is a valid tool for improving the adequacy of referrals to dermatology and reduces costs in secondary care.
• Outcomes:
  We collected a total of 82 images from 51 patients using smartphones and dermatoscopes. Patients with pigmented lesions tend to have more images on average because they include both clinical and dermatoscopic pictures. The use of the device on the collected images lead to both a high sensitivity (100%) and specificity (76%) for the malignancy detection task. Analysis of Teledermatology Cases: Dermatologists opted for in-person consultations in 71% of cases. 29% of cases were addressed directly, with 53% related to seborrheic keratosis. Performance on Low-Quality Images: Sensitivity on low-quality images: 100%, specificity: approximately 63%. Cost reduction potential by avoiding unnecessary consultations based on algorithm outcomes.
• Data analysis plan:
  Each variable will be characterized using frequency distributions for qualitative variables and central tendency statistics such as mean and median and variability statistics such as standard deviation (S.D.) or interquartile range for quantitative variables according to their distributional characteristics. Sensitivity, specificity, positive and negative predictive values (PPV and NPV) and likelihood ratios (LR+ and LR-) will be calculated by comparing both the results obtained using the device and those obtained with the referral criteria of primary care physicians with the criteria used by specialists, considered the gold standard. Analyses will be performed using appropriate statistical software, SPSS version 23.0 and STATA 13.0. Values of p<0.05 will be considered significant.

Documents

Study, other information

CEIm_LEGIT.HEALTH_DAO_Derivación_PS2022074-5abceddcca6112d4daba41ad6bcb3309

https://www.biocrucesbizkaia.org/servicios/medtech