Innovation in Actinic Keratosis Assessment: Artificial Intelligence-Based Approach to LC-OCT PRO Score Evaluation

Actinic keratosis (AK) is a common skin cancer in situ that can progress to invasive SCC. Line-field confocal optical coherence tomography (LC-OCT) has emerged as a non-invasive imaging technique that can aid in diagnosis. Recently, machine-learning algorithms have been developed that can automatica...

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Vydáno v:Cancers Ročník 15; číslo 18; s. 4457
Hlavní autoři: Daxenberger, Fabia, Deußing, Maximilian, Eijkenboom, Quirine, Gust, Charlotte, Thamm, Janis, Hartmann, Daniela, French, Lars, Welzel, Julia, Schuh, Sandra, Sattler, Elke
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 07.09.2023
MDPI
Témata:
Actinic keratosis
Algorithms
Artificial intelligence
Classification
Data mining
ddc:610
Dermatology
Diagnosis
Histology
Invasiveness
Keratosis
Lesions
Machine learning
Neural networks
Pilot projects
Skin
Skin cancer
Tomography
ISSN:2072-6694, 2072-6694
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Shrnutí:Actinic keratosis (AK) is a common skin cancer in situ that can progress to invasive SCC. Line-field confocal optical coherence tomography (LC-OCT) has emerged as a non-invasive imaging technique that can aid in diagnosis. Recently, machine-learning algorithms have been developed that can automatically assess the PRO score of AKs based on the dermo-epidermal junction’s (DEJ’s) protrusion on LC-OCT images. A dataset of 19.898 LC-OCT images from 80 histologically confirmed AK lesions was used to test the performance of a previous validated artificial intelligence (AI)-based LC-OCT assessment algorithm. AI-based PRO score assessment was compared to the imaging experts’ visual score. Additionally, undulation of the DEJ, the number of protrusions detected within the image, and the maximum depth of the protrusions were computed. Our results show that AI-automated PRO grading is highly comparable to the visual score, with an agreement of 71.3% for the lesions evaluated. Furthermore, this AI-based assessment was significantly faster than the regular visual PRO score assessment. The results confirm our previous findings of the pilot study in a larger cohort that the AI-based grading of LC-OCT images is a reliable and fast tool to optimize the efficiency of visual PRO score grading. This technology has the potential to improve the accuracy and speed of AK diagnosis and may lead to better clinical outcomes for patients.
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These authors contributed equally to this work.
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers15184457