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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| Published in: | Cancers Vol. 15; no. 18; p. 4457 |
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| Main Authors: | , , , , , , , , , |
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07.09.2023
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| ISSN: | 2072-6694, 2072-6694 |
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| Abstract | 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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| AbstractList | Simple SummaryIn this study, we evaluated the performance of a previous validated artificial intelligence-based assessment algorithm using line-field confocal optical coherence tomography (LC-OCT) to diagnose actinic keratosis (AK). The AI system accurately graded AK lesions in a large patient cohort and showed high agreement with visual assessments by experts. This non-invasive and fast AI-based approach has the potential to improve the efficiency and accuracy of AK diagnosis, leading to better clinical outcomes for patients.AbstractActinic 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. 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.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. In this study, we evaluated the performance of a previous validated artificial intelligence-based assessment algorithm using line-field confocal optical coherence tomography (LC-OCT) to diagnose actinic keratosis (AK). The AI system accurately graded AK lesions in a large patient cohort and showed high agreement with visual assessments by experts. This non-invasive and fast AI-based approach has the potential to improve the efficiency and accuracy of AK diagnosis, leading to better clinical outcomes for patients. 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. In this study, we evaluated the performance of a previous validated artificial intelligence-based assessment algorithm using line-field confocal optical coherence tomography (LC-OCT) to diagnose actinic keratosis (AK). The AI system accurately graded AK lesions in a large patient cohort and showed high agreement with visual assessments by experts. This non-invasive and fast AI-based approach has the potential to improve the efficiency and accuracy of AK diagnosis, leading to better clinical outcomes for patients. 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. |
| Audience | Academic |
| Author | Maximilian Deußing Daniela Hartmann Janis Thamm Elke Sattler Sandra Schuh Julia Welzel Quirine Eijkenboom Fabia Daxenberger Charlotte Gust Lars French |
| AuthorAffiliation | 3 Department of Dermatology & Cutaneous Surgery, Miller School of Medicine, University of Miami, Miami, FL 33136, USA 1 Department of Dermatology and Allergy, University Hospital, Ludwig Maximilian University of Munich, 80337 Munich, Germany elke.sattler@med.uni-muenchen.de (E.C.S.) 2 Department of Dermatology and Allergology, University Hospital, University of Augsburg, 86179 Augsburg, Germany |
| AuthorAffiliation_xml | – name: 1 Department of Dermatology and Allergy, University Hospital, Ludwig Maximilian University of Munich, 80337 Munich, Germany elke.sattler@med.uni-muenchen.de (E.C.S.) – name: 3 Department of Dermatology & Cutaneous Surgery, Miller School of Medicine, University of Miami, Miami, FL 33136, USA – name: 2 Department of Dermatology and Allergology, University Hospital, University of Augsburg, 86179 Augsburg, Germany |
| Author_xml | – sequence: 1 givenname: Fabia surname: Daxenberger fullname: Daxenberger, Fabia – sequence: 2 givenname: Maximilian orcidid: 0000-0003-4935-9877 surname: Deußing fullname: Deußing, Maximilian – sequence: 3 givenname: Quirine orcidid: 0009-0006-8014-4152 surname: Eijkenboom fullname: Eijkenboom, Quirine – sequence: 4 givenname: Charlotte surname: Gust fullname: Gust, Charlotte – sequence: 5 givenname: Janis surname: Thamm fullname: Thamm, Janis – sequence: 6 givenname: Daniela orcidid: 0000-0002-1002-8133 surname: Hartmann fullname: Hartmann, Daniela – sequence: 7 givenname: Lars surname: French fullname: French, Lars – sequence: 8 givenname: Julia orcidid: 0000-0002-6099-7418 surname: Welzel fullname: Welzel, Julia – sequence: 9 givenname: Sandra orcidid: 0000-0002-1470-7619 surname: Schuh fullname: Schuh, Sandra – sequence: 10 givenname: Elke surname: Sattler fullname: Sattler, Elke |
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| Snippet | 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... In this study, we evaluated the performance of a previous validated artificial intelligence-based assessment algorithm using line-field confocal optical... Simple SummaryIn this study, we evaluated the performance of a previous validated artificial intelligence-based assessment algorithm using line-field confocal... |
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| SubjectTerms | 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 |
| Title | Innovation in Actinic Keratosis Assessment: Artificial Intelligence-Based Approach to LC-OCT PRO Score Evaluation |
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