Microsatellite instability (MSI)/mismatch repair deficiency (dMMR) is a key genetic feature which should be tested in every patient with colorectal cancer (CRC) according to medical guidelines. Artificial intelligence (AI) methods can detect MSI/dMMR directly in routine pathology slides, but the test performance has not been systematically investigated with predefined test thresholds.
The challenging task of skin cancer classification via artificial intelligence (AI) is a combined effort of dermatologists, patients, and the machine-learning community.1,2 A classifier in this case is a software based on AI that assigns skin photographs to categories (ie, benign or malignant).
Background: Artificial intelligence has shown promise in numerous experimental studies, particularly in skin cancer diagnostics. Translation of these findings into the clinic is the logical next step. This translation can only be successful if patients’ concerns and questions are addressed suitably. We therefore conducted a survey to evaluate the patients’ view of artificial intelligence in melanoma diagnostics in Germany. Participants and Methods: A web-based questionnaire was designed using LimeSurvey, sent by e-mail to university hospitals and melanoma support groups and advertised on social media. The anonymous questionnaire evaluated patients’ expectations and concerns towards artificial intelligence in general as well as their attitudes towards different application scenarios. Descriptive analysis was performed with expression of categorical variables as percentages and 95% confidence intervals. Statistical tests were performed to investigate associations between sociodemographic data and selected items of the questionnaire. Results: 298 people (154 with melanoma diagnosis, 143 without) responded to the questionnaire. About 94% [95% CI = 0.913 – 0.967] of respondents supported the use of artificial intelligence in medical approaches. 88% [95% CI = 0.846 – 0.919] would even make their own health data anonymously available for the further development of AI-based applications in medicine. Only 41% [95% CI = 0.350 – 0.462] of respondents were amenable to the use of artificial intelligence as stand-alone system, 94% [95% CI = 0.917 – 0.969 to its use as assistant system for physicians. In sub-group analyses, only minor differences were detectable. Respondents with a previous history of melanoma were more amenable to the use of AI applications for early detection even at home. They would prefer an application scenario where physician and AI classify the lesions independently. With respect to AI-based applications in medicine, patients were concerned about insufficient data protection, impersonality and susceptibility to errors, but expected faster, more precise and unbiased diagnostics, less diagnostic errors and support for physicians. Conclusions: The vast majority of participants exhibited a positive attitude towards the use of artificial intelligence in melanoma diagnostics, especially as an assistance system.
Zusammenfassung Das maligne Melanom ist diejenige Form von Hautkrebs, an der die meisten Menschen sterben. Im Frühstadium ist das Melanom gut behandelbar, eine Früherkennung ist also lebenswichtig. Kritiker bemängeln, dass seit der bundesweiten Einführung des Hautkrebs‐Screenings häufiger Melanome diagnostiziert werden, die Sterblichkeit am malignen Melanom jedoch nicht zurückgegangen ist. Sie führen dies vor allem auf Überdiagnosen zurück. Ein Grund ist die zum Teil komplexe Unterscheidung zwischen benignen und malignen Läsionen. Hinzu kommt, dass es auch Übergangsformen zwischen eindeutig gut‐ oder bösartigen Läsionen geben kann, und dass einige bösartige Läsionen so wenig aggressiv wachsen, dass sie nie lebensbedrohlich geworden wären. Bisher kann mangels entsprechender Biomarker nicht festgestellt werden, bei welchen Melanomen dies der Fall ist. Auch die Progressionswahrscheinlichkeit eines In‐situ‐Melanoms zu einem invasiven Tumor kann bisher nicht sicher beurteilt werden. Die Konsequenzen für überdiagnostizierte benigne Läsionen sind unnötige psychische und körperliche Belastungen für die Betroffenen und anfallende Therapiekosten. Umgekehrt können Unterdiagnosen zu gravierenden Einschränkungen der Prognose der Betroffenen und zur Notwendigkeit belastender(er) Therapien führen. Präzisere Diagnosemöglichkeiten könnten die Anzahl der korrekten Diagnosen erhöhen. Hier haben sich in Studien mit auf künstlicher Intelligenz basierenden Assistenzsystemen bereits erste Erfolge gezeigt, die allerdings noch in die klinische und pathologische Routine übertragen werden müssen.
Because exposure to UV radiation early in life is an important risk factor for melanoma development, reducing UV exposure in children and adolescents is of paramount importance. New interventions are urgently required.To determine the effect of the free face-aging mobile app Sunface on the skin cancer protection behavior of adolescents.This cluster-randomized clinical trial included a single intervention and a 6-month follow-up from February 1 to November 30, 2018. Randomization was performed on the class level in 52 school classes within 8 public secondary schools (grades 9-12) in Itauna, Southeast Brazil. Data were analyzed from May 1 to October 10, 2019.In a classroom seminar delivered by medical students, adolescents' selfies were altered by the app to show UV effects on their future faces and were shown in front of their class, accompanied by information about UV protection. Information about relevant parameters was collected via anonymous questionnaires before and 3 and 6 months after the intervention.The primary end point of the study was the difference in daily sunscreen use at 6 months of follow-up. Secondary end points included the difference in daily sunscreen use at 3 months of follow-up, at least 1 skin self-examination within 6 months, and at least 1 tanning session in the preceding 30 days. All analyses were predefined and based on intention to treat. Cluster effects were taken into account.Participants included 1573 pupils (812 girls [51.6%] and 761 boys [48.4%]; mean [SD] age, 15.9 [1.3] years) from 52 school classes. Daily sunscreen use increased from 110 of 734 pupils (15.0%) to 139 of 607 (22.9%; P < .001) at the 6-month follow-up in the intervention group. The proportion of pupils performing at least 1 skin self-examination in the intervention group rose from 184 of 734 (25.1%) to 300 of 607 (49.4%; P < .001). Use of tanning decreased from 138 of 734 pupils (18.8%) to 92 of 607 (15.2%; P = .04). No significant changes were observed in the control group. The intervention was more effective for female students (number needed to treat for the primary end point: 8 for girls and 31 for boys).These findings suggest that interventions based on face-aging apps may increase skin cancer protection behavior in Brazilian adolescents. Further studies are required to maximize the effect and to investigate the generalizability of the effects.ClinicalTrials.gov Identifier: NCT03178240.
BACKGROUND Artificial intelligence (AI) has shown potential to improve diagnostics of various diseases, especially for early detection of skin cancer. Studies have yet to investigate the clear application of AI technology in clinical practice or determine the added value for younger user groups. Translation of AI-based diagnostic tools can only be successful if they are accepted by potential users. Young adults as digital natives may offer the greatest potential for successful implementation of AI into clinical practice, while at the same time, representing the future generation of skin cancer screening participants. OBJECTIVE We conducted an anonymous online survey to examine how and to what extent individuals are willing to accept AI-based mobile apps for skin cancer diagnostics. We evaluated preferences and relative influences of concerns, with a focus on younger age groups. METHODS We recruited participants below 35 years of age using three social media channels—Facebook, LinkedIn, and Xing. Descriptive analysis and statistical tests were performed to evaluate participants’ attitudes toward mobile apps for skin examination. We integrated an adaptive choice-based conjoint to assess participants’ preferences. We evaluated potential concerns using maximum difference scaling. RESULTS We included 728 participants in the analysis. The majority of participants (66.5%, 484/728; 95% CI 0.631-0.699) expressed a positive attitude toward the use of AI-based apps. In particular, participants residing in big cities or small towns (<i>P</i>=.02) and individuals that were familiar with the use of health or fitness apps (<i>P</i>=.02) were significantly more open to mobile diagnostic systems. Hierarchical Bayes estimation of the preferences of participants with a positive attitude (n=484) revealed that the use of mobile apps as an assistance system was preferred. Participants ruled out app versions with an accuracy of ≤65%, apps using data storage without encryption, and systems that did not provide background information about the decision-making process. However, participants did not mind their data being used anonymously for research purposes, nor did they object to the inclusion of clinical patient information in the decision-making process. Maximum difference scaling analysis for the negative-minded participant group (n=244) showed that data security, insufficient trust in the app, and lack of personal interaction represented the dominant concerns with respect to app use. CONCLUSIONS The majority of potential future users below 35 years of age were ready to accept AI-based diagnostic solutions for early detection of skin cancer. However, for translation into clinical practice, the participants’ demands for increased transparency and explainability of AI-based tools seem to be critical. Altogether, digital natives between 18 and 24 years and between 25 and 34 years of age expressed similar preferences and concerns when compared both to each other and to results obtained by previous studies that included other age groups.
BACKGROUND Smoking is the largest preventable cause of mortality in Brazil. Education Against Tobacco (EAT) is a network of more than 3500 medical students and physicians across 14 countries who volunteer for school-based smoking prevention programs. EAT educates 50,000 adolescents per year in the classroom setting. A recent quasi-experimental study conducted in Germany showed that EAT had significant short-term smoking cessation effects among adolescents aged 11 to 15 years. OBJECTIVE The aim is to measure the long-term effectiveness of the most recent version of the EAT curriculum in Brazil. METHODS A randomized controlled trial was conducted among 2348 adolescents aged 12 to 21 years (grades 7-11) at public secondary schools in Brazil. The prospective experimental design included measurements at baseline and at 6 and 12 months postintervention. The study groups comprised randomized classes receiving the standardized EAT intervention (90 minutes of mentoring in a classroom setting) and control classes in the same schools (no intervention). Data were collected on smoking status, gender, social aspects, and predictors of smoking. The primary endpoint was the difference in the change in smoking prevalence between the intervention group and the control group at 12-month follow-up. RESULTS From baseline to 12 months, the smoking prevalence increased from 11.0% to 20.9% in the control group and from 14.1% to 15.6% in the intervention group. This difference was statistically significant (P<.01). The effects were smaller for females (control 12.4% to 18.8% vs intervention 13.1% to 14.6%) than for males (control 9.1% to 23.6% vs intervention 15.3% to 16.8%). Increased quitting rates and prevented onset were responsible for the intervention effects. The differences in change in smoking prevalence from baseline to 12 months between the intervention and control groups were increased in students with low school performance. CONCLUSIONS To our knowledge, this is the first randomized trial on school-based tobacco prevention in Brazil that shows significant long-term favorable effects. The EAT program encourages quitting and prevents smoking onset, especially among males and students with low educational background. CLINICALTRIAL ClinicalTrials.gov NCT02725021; https://clinicaltrials.gov/ct2/show/NCT02725021
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