Skin cancer risk self-assessment using AI as a mass screening tool
Skin cancer is one of the most common cancers on the planet. The lack of noticeable symptoms, inaccessibility of professional dermatology expertise, and low population awareness prevent early detection, which is vital for successful treatment. We proposed the free AI-powered self-examination tool al...
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| Published in: | Informatics in medicine unlocked Vol. 38; p. 101223 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
2023
Elsevier |
| Subjects: | |
| Online Access: | Get full text |
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| Summary: | Skin cancer is one of the most common cancers on the planet. The lack of noticeable symptoms, inaccessibility of professional dermatology expertise, and low population awareness prevent early detection, which is vital for successful treatment.
We proposed the free AI-powered self-examination tool allowing everyone to perform regular skin checkups with a smartphone's photo coupled with the new set of risk factors suitable for self-assessment. While most of the existing AI solutions focus on dermoscopic images and assisting staff in medical facilities, we in contrast designed the ensemble of deep convolutional neural networks to be used by non-specialists for assessing self-taken skin clinical images via mobile app. We aimed to distinguish between malignant and benign neoplasms only, encouraging people to follow up with dermatologists for an in-depth physical examination in case of discovered skin cancer risk.
400000+ clinical skin images coupled with risk factors and demographic data have been collected, processed by AI, and validated by dermatology experts, which made it one of the biggest mass-screening campaigns for skin cancer disease in the Russian Federation. 9321 malignant cases were discovered including 5230 melanomas.
The comprehensive analysis of mass-screening results was conducted for the following regions in Russian Federation: Moscow and Moscow region, Nizhny Novgorod, St. Petersburg and Leningradsky region, and Krasnodar region. We discovered the significantly higher melanoma cancer prevalence rate (number of cases per 100 000 people is 5.43 cases for males and 11.88 cases for females while statistical bulletin reports 4.12 cases in 2021) and much younger average age of skin cancer development (melanoma: 46.78 years for males and 41.35 years for females vs official numbers of 61.5 years and 62.1 years correspondingly; BCC/SCC: 56.28 years for males and 55.98 years for females vs official numbers of 68.2 years and 70.2 years correspondingly) comparing to statistical bulletins. Our findings demonstrate that the female population is at risk almost 5 years earlier and reveal the most common locations for malignant tumors: face, upper body, and shoulders. Our new skin cancer risk self-assessment criteria (20+ spots on arm and solarium visits) associate with the earlier melanoma development in the female population and should be considered in further skin cancer studies as risk factors.
The proposed approach proved to be a cost-efficient way of doing mass skin cancer screenings, which have not been considered a feasible option before. The presented self-assessment risk factors along with the revised average age of malignant tumors development and prevalence rates should be considered in skin cancer surveillance.
•400 000+ clinical skin images coupled with risk factors collected, processed by AI, and validated by dermatology experts.•Deep convolutional neural networks for detecting melanoma and non-melanoma skin cancer in non-dermoscopic images.•Discovered the significantly higher prevalence rate and much younger average age of melanoma vs official stats.•Proposed self-assessment risk criteria (20+ spots on arm, solarium visits) associate with earlier female melanoma cases. |
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| ISSN: | 2352-9148 2352-9148 |
| DOI: | 10.1016/j.imu.2023.101223 |