Patients poorly recognize lesions of concern that are malignant melanomas: is self-screening the correct advice?

Background: Australia is known for its outdoor culture, with a large percentage of its population engaging in outdoor recreational activities, aquatic, non-aquatic and outdoor occupational activities. However, these outdoor enthusiasts face increased exposure to ultraviolet radiation (UVR), leading to a higher risk of skin cancer, including malignant melanoma (MM). Over the past 40 years, there has been a significant rise in skin cancer rates in Australia, with two out of three Australians expected to develop some form of skin cancer by age 70. Currently, skin cancer examinations are not endorsed in asymptomatic or low-risk individuals in Australia, with only high-risk individuals recommended to undergo regular skin examinations. Notably, the Melanoma Institute Australia suggests that one-half of patients identify MMs themselves, although this claim appears to be based on limited Australian data which may not reflect contemporary practice. Therefore this study sought to determine the percentage of patients who were able to self-identify MMs as lesions of concern when presenting for a skin cancer examination. Methods: Multi-site, cross-sectional study design incorporating a descriptive survey and total body skin cancer screening, including artificial intelligence by a skin cancer doctor. Results: A total of 260 participants with suspect MM lesions were biopsied, with 83 (31.9%) found to be melanomas. Of the true positive MMs only a small percentage of participants (21.7% specificity) correctly had concerns about the suspect lesion being a MM. These MMs were located primarily on the back (44.4%), shoulder (11.1%) and upper leg (11.1%). There was no significant difference in the size between those participants aware of a MM versus those who were not (P = 0.824, 24.6 vs 23.4 mm2). Significantly more males identified lesions of concern that were MMs as compared to females (P = 0.008, 61.1% vs 38.9%, respectively). With regard to true negatives males and females were similar (52.1% vs 47.9%, respectively). With regard to false negatives (n = 65), a greater percentage of males than females did not recognize the MM as a lesion of concern (66.2% vs 33.8%, respectively). Participants were more likely to correctly identify an invasive MM as opposed to an in situ MM (27.3% versus 21.3%). Conclusions: Only a small percentage of participants in this study were able to self-identify either in situ or invasive MM as a lesion of concern with a tendency to identify the more advanced, thicker MMs. Given that MM is associated with a high mortality and cost of treatment, particularly when invasive, the inability of lay persons to identify these cancerous lesions will likely lead to delayed treatment and a possible adverse outcome. We believe the current melanoma screening practices in Australian general practice should be revisited to improve patient outcomes with regard to MM. Additionally, prevention campaigns should include images and primary risk factors for MM.

Performance of Commercial Dermatoscopic Systems That Incorporate Artificial Intelligence for the Identification of Melanoma in General Practice: A Systematic Review.

BACKGROUND: Cutaneous melanoma remains an increasing global public health burden, particularly in fair-skinned populations. Advancing technologies, particularly artificial intelligence (AI), may provide an additional tool for clinicians to help detect malignancies with a more accurate success rate. This systematic review aimed to report the performance metrics of commercially available convolutional neural networks (CNNs) tasked with detecting MM. METHODS: A systematic literature search was performed using CINAHL, Medline, Scopus, ScienceDirect and Web of Science databases. RESULTS: A total of 16 articles reporting MM were included in this review. The combined number of melanomas detected was 1160, and non-melanoma lesions were 33,010. The performance of market-approved technology and clinician performance for classifying melanoma was highly heterogeneous, with sensitivity ranging from 16.4 to 100.0%, specificity between 40.0 and 98.3% and accuracy between 44.0 and 92.0%. Less heterogeneity was observed when clinicians worked in unison with AI, with sensitivity ranging between 83.3 and 100.0%, specificity between 83.7 and 87.3%, and accuracy between 86.4 and 86.9%. CONCLUSION: Instead of focusing on the performance of AI versus clinicians for classifying melanoma, more consistent performance has been obtained when clinicians' work is supported by AI, facilitating management decisions and improving health outcomes.

Automated extraction of heart rate variability from magnetoencephalography signals.

Magnetoencephalography (MEG) measures magnetic fluctuations in the brain generated by neural processes, some of which, such as cardiac signals, are generally removed as artifacts and discarded. However, heart rate variability (HRV) has long been regarded as a biomarker related to autonomic function, suggesting the cardiac signal in MEG contains valuable information that can provide supplemental health information about a patient. To enable access to these ancillary HRV data, we created an automated extraction tool capable of capturing HRV directly from raw MEG data with artificial intelligence. Five scans were conducted with simultaneous MEG and electrocardiogram (ECG) acquisition, which provides a ground truth metric for assessing our algorithms and data processing pipeline. In addition to directly comparing R-peaks between the MEG and ECG signals, this work explores the variation of the corresponding HRV output in time, frequency, and non-linear domains. After removing outlier intervals and aligning the ECG and derived cardiac MEG signals, the RMSE between the RR-intervals of each was RMSE1 = 2 ms, RMSE2 = 2 ms, RMSE3 = 8 ms, RMSE4 = 4 ms, RMSE5 = 13 ms. The findings indicate that cardiac artifacts from MEG data carry sufficient signal to approximate an individual's HRV metrics.

Performance of an automated total body mapping algorithm to detect melanocytic lesions of clinical relevance.

IMPORTANCE: Total body photography for skin cancer screening is a well-established tool allowing documentation and follow-up of the entire skin surface. Artificial intelligence-based systems are increasingly applied for automated lesion detection and diagnosis. DESIGN AND PATIENTS: In this prospective observational international multicentre study experienced dermatologists performed skin cancer screenings and identified clinically relevant melanocytic lesions (CRML, requiring biopsy or observation). Additionally, patients received 2D automated total body mapping (ATBM) with automated lesion detection (ATBM master, Fotofinder Systems GmbH). Primary endpoint was the percentage of CRML detected by the bodyscan software. Secondary endpoints included the percentage of correctly identified "new" and "changed" lesions during follow-up examinations. RESULTS: At baseline, dermatologists identified 1075 CRML in 236 patients and 999 CRML (92.9%) were also detected by the automated software. During follow-up examinations dermatologists identified 334 CRMLs in 55 patients, with 323 (96.7%) also being detected by ATBM with automated lesions detection. Moreover, all new (n = 13) or changed CRML (n = 24) during follow-up were detected by the software. Average time requirements per baseline examination was 14.1 min (95% CI [12.8-15.5]). Subgroup analysis of undetected lesions revealed either technical (e.g. covering by clothing, hair) or lesion-specific reasons (e.g. hypopigmentation, palmoplantar sites). CONCLUSIONS: ATBM with lesion detection software correctly detected the vast majority of CRML and new or changed CRML during follow-up examinations in a favourable amount of time. Our prospective international study underlines that automated lesion detection in TBP images is feasible, which is of relevance for developing AI-based skin cancer screenings.

Implementation of artificial intelligence for the detection of cutaneous melanoma within a primary care setting: prevalence and types of skin cancer in outdoor enthusiasts.

Background: There is enthusiasm for implementing artificial intelligence (AI) to assist clinicians detect skin cancer. Performance metrics of AI from dermoscopic images have been promising, with studies documenting sensitivity and specificity values equal to or superior to specialists for the detection of malignant melanomas (MM). Early detection rates would particularly benefit Australia, which has the worlds highest incidence of MM per capita. The detection of skin cancer may be delayed due to late screening or the inherent difficulty in diagnosing early skin cancers which often have a paucity of clinical features and may blend into sun damaged skin. Individuals who participate in outdoor sports and recreation experience high levels of intermittent ultraviolet radiation (UVR), which is associated with the development of skin cancer, including MM. This research aimed to assess the prevalence of skin cancer in individuals who regularly participate in activities outdoors and to report the performance parameters of a commercially available AI-powered software to assess the predictive risk of MM development. Methods: Cross-sectional study design incorporating a survey, total body skin cancer screening and AI-embedded software capable of predictive scoring of queried MM. Results: A total of 423 participants consisting of surfers (n = 108), swimmers (n = 60) and walkers/runners (n = 255) participated. Point prevalence for MM was highest for surfers (6.48%), followed by walkers/runners (4.3%) and swimmers (3.33%) respectively. When compared to the general Australian population, surfers had the highest odds ratio (OR) for MM (OR 119.8), followed by walkers/runners (OR 79.74), and swimmers (OR 61.61) rounded out the populations. Surfers and swimmers reported comparatively lower lifetime hours of sun exposure (5,594 and 5,686, respectively) but more significant amounts of activity within peak ultraviolet index compared with walkers/runners (9,554 h). A total of 48 suspicious pigmented lesions made up of histopathology-confirmed MM (n = 15) and benign lesions (n = 33) were identified. The performance of the AI from this clinical population was found to have a sensitivity of 53.33%, specificity of 54.44% and accuracy of 54.17%. Conclusions: Rates of both keratinocyte carcinomas and MM were notably higher in aquatic and land-based enthusiasts compared to the general Australian population. These findings further highlight the clinical importance of sun-safe protection measures and regular skin screening in individuals who spend significant time outdoors. The use of AI in the early identification of MM is promising. However, the lower-than-expected performance metrics of the AI software used in this study indicated reservations should be held before recommending this particular version of this AI software as a reliable adjunct for clinicians in skin imaging diagnostics in patients with potentially sun damaged skin.

The Cure That Lies Within: The Mind-Body Connection in Orthopaedics.

The mind and the body are clearly intertwined in ways that are only now being discovered. In the orthopaedic world, injuries and diseases are often classified and described in a very organized, discrete fashion-The radius is fractured, the ACL or meniscus or rotator cuff is torn, the ankle is sprained, and/or the lumbar spine has a disc herniation. Although it is, in many ways, almost comforting to think about injuries or orthopaedic issues in this manner, what about the many patients who fail to fall into this classification? What about the thousands of patients with severe unexplained chronic pain or patients who just are not improving with the typical treatment algorithm. What about patients who present with multiple overlapping symptoms that do not fall into any of the classic diagnosis patterns? The mismatch between the actual health needs of typical patients and the standard acute medical response produces an immense waste of medical resources and incredible frustration for both the patient and the provider and creates a real risk that acute conditions will go untreated and become chronic. After more than a decade of traditional orthopaedic and musculoskeletal practice, its tremendous benefits as well as its limitations have become apparent. These limitations have sparked a search for integration of mind-body considerations to fill some of these gaps. Although this can prove to be quite challenging in today's healthcare world of maximizing volume and decreasing costs, it has proven to be an invaluable resource for both personal growth and patient and family satisfaction. The goals of this 2-part article are to dissect the relatively new concept of the mind-body connection in orthopaedics. The article aims to provide a framework that illustrates how the mind will predictably create objective observable phenomena in the body. The central focus of this framework is the role of the sympathetic nervous system and its effect on the chemistry, biomechanics, and appearance of various tissues in the body. Further identified are factors contributing to the aberrant emotional response as a means to empower practitioners and patients in recognizing the link between negative perception and observable symptoms. Our hope is to ultimately introduce a model of empowerment that when presented to a patient/family can produce a proactive response and, in turn, enhance current orthopaedic and pain management practices.