Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting

Abstract

Skin cancer is currently the most common type of cancer among Caucasians. The increase in life expectancy, along with new diagnostic tools and treatments for skin cancer, has resulted in unprecedented changes in patient care and has generated a great burden on healthcare systems. Early detection of skin tumors is expected to reduce this burden. Artificial intelligence (AI) algorithms that support skin cancer diagnoses have been shown to perform at least as well as dermatologists’ diagnoses. Recognizing the need for clinically and economically efficient means of diagnosing skin cancers at early stages in the primary care attention, we developed an efficient computer-aided diagnosis (CAD) system to be used by primary care physicians (PCP). Additionally, we developed a smartphone application with a protocol for data acquisition (i.e., photographs, demographic data and short clinical histories) and AI algorithms for clinical and dermoscopic image classification. For each lesion analyzed, a report is generated, showing the image of the suspected lesion and its respective Heat Map; the predicted probability of the suspected lesion being melanoma or malignant; the probable diagnosis based on that probability; and a suggestion on how the lesion should be managed. The accuracy of the dermoscopy model for melanoma was 89.3%, and for the clinical model, 84.7% with 0.91 and 0.89 sensitivity and 0.89 and 0.83 specificity, respectively. Both models achieved an area under the curve (AUC) above 0.9. Our CAD system can screen skin cancers to guide lesion management by PCPs, especially in the contexts where the access to the dermatologist can be difficult or time consuming. Its use can enable risk stratification of lesions and/or patients and dramatically improve timely access to specialist care for those requiring urgent attention.

Automation in Contemporary Clinical Information Systems: a Survey of AI in Healthcare Settings

“…Another example is the use of non-clinical data such as traffic volume and socioeconomic status for predicting the risk of asthma exacerbations [18]. Other problem-driven examples include dermatology apps specifically developed for people of colour [27,55], a radiology examination instruction system to support COVID-19 triage in a predominantly Spanish-speaking Latino community [29], and AI systems for trainee clinicians [36,70].…”

“…Duan et al [26] demonstrated improved image quality, reduced noise and processing time for CT images to assess colon cancer. Aimed at primary care physicians in Brazil, Giavina-Bianchi et al [27] demonstrated algorithms for melanoma screening using both dermascope and smart phone images (accuracy: 89%, 85%; sensitivity: 91%, 89%; specificity: 89%, 83%).…”

Basic principles of artificial intelligence in dermatology explained using melanoma

“…68 Most studies have inclusion criteria for patients or images, thus making it harder to generalize the results, for example due to lack of data from non-caucasian/non-asian patients. 69 For CNNs, it can be hard to apply the rules learned from a specific set of data to other sets of data. This lack of generalizability of CNNs has been well demonstrated in the past.…”

“…Many mobile applications touted as a diagnostic tool often lack evidence of safety and efficacy 68 . Most studies have inclusion criteria for patients or images, thus making it harder to generalize the results, for example due to lack of data from non‐caucasian/non‐asian patients 69 . For CNNs, it can be hard to apply the rules learned from a specific set of data to other sets of data.…”

Grundprinzipien der künstlichen Intelligenz in der Dermatologie erklärt am Beispiel des Melanoms

“…Bei vielen mobilen Anwendungen, die als Diagnoseinstrument angepriesen werden, fehlen oft Nachweise für die Sicherheit und Wirksamkeit 68 . Die meisten Studien haben Einschlusskriterien für Patienten oder Bilder, was eine Verallgemeinerung der Ergebnisse erschwert, da beispielsweise keine Daten von nicht‐weißen/nicht‐asiatischen Patienten vorliegen 69 . Für CNN kann es schwierig sein, die aus einem bestimmten Datensatz extrahierten Regeln auf andere Datensätze anzuwenden.…”