Dynamics of the Neural Network Accuracy in the Context of Modernization of the Algorithms of Skin Pathology Recognition

BACKGROUND: The lack of objective methodologies and open datasets for the evaluation of the algorithms complicates the objective evaluation by specialists and hinders the widespread use of this technology in health care. The purpose of this study was to estimate the accuracy of Skinive's algorithm 2020 version, then, after an algorithm improvement in 2020–2021, to show a statistically significant decrease in neural network errors in the risk assessment of skin pathologies in 2021. METHODS: The Skinive neural network uses a machine-learning algorithm to calculate the risk rating of skin pathologies. For this study, we used Skinive's algorithm 2020 and 2021 versions trained on 64,000 and 115,000 images, respectively. Three validation datasets were used to assess the sensitivity of the algorithm: precancer + cancer, viral skin pathology, acne, containing 285 images in each set. The specificity has been calculated on a separate validation set containing 6,000 benign neoplasm cases. RESULTS: The sensitivity of the Skinive neural network in detecting malignant neoplasms was 89.1% and 95.4% in 2020 and 2021, respectively. The specificity of Skinive's neural network in determining benign neoplasms was 95.3% in 2020 and 97.9% in 2021. For all skin neoplasms, in 2020, the sensitivity was 95.3%, and specificity was 93.5%; in 2021, these were 97.9% and 97.1%, respectively. CONCLUSIONS: The results of sensitivity and specificity of the Skinive neural network indicate that the algorithm is highly accurate in detecting various neoplasms and skin diseases. After improving the algorithm, we showed a statistically significant decrease in the number of neural network errors in determining the risks of skin pathologies.