The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For ...melanoma, the literature reports on 25–26% of discordance for classifying a benign nevus versus malignant melanoma. A recent study indicated the potential of deep learning to lower these discordances. However, the performance of deep learning in classifying histopathologic melanoma images was never compared directly to human experts. The aim of this study is to perform such a first direct comparison.
A total of 695 lesions were classified by an expert histopathologist in accordance with current guidelines (350 nevi/345 melanoma). Only the haematoxylin & eosin (H&E) slides of these lesions were digitalised via a slide scanner and then randomly cropped. A total of 595 of the resulting images were used to train a convolutional neural network (CNN). The additional 100 H&E image sections were used to test the results of the CNN in comparison to 11 histopathologists. Three combined McNemar tests comparing the results of the CNNs test runs in terms of sensitivity, specificity and accuracy were predefined to test for significance (p < 0.05).
The CNN achieved a mean sensitivity/specificity/accuracy of 76%/60%/68% over 11 test runs. In comparison, the 11 pathologists achieved a mean sensitivity/specificity/accuracy of 51.8%/66.5%/59.2%. Thus, the CNN was significantly (p = 0.016) superior in classifying the cropped images.
With limited image information available, a CNN was able to outperform 11 histopathologists in the classification of histopathological melanoma images and thus shows promise to assist human melanoma diagnoses.
•A convolutional neural network (CNN) was trained with 595 histopathologic images of melanoma and nevi.•In a direct comparison, the CNN and 11 histopathologists classified a test set of 100 additional histopathologic images (1:1 melanoma/nevi).•The CNN systematically outperformed the 11 histopathologists in terms of overall accuracy, sensitivity and specificity (p = 0.016).
In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial ...intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification.
Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300 biopsy-verified skin lesions into those five classes. Taking into account the certainty of the decisions, the two independently determined diagnoses were combined to a new classifier with the help of a gradient boosting method. The primary end-point of the study was the correct classification of the images into five designated categories, whereas the secondary end-point was the correct classification of lesions as either benign or malignant (binary classification).
Regarding the multiclass task, the combination of man and machine achieved an accuracy of 82.95%. This was 1.36% higher than the best of the two individual classifiers (81.59% achieved by the CNN). Owing to the class imbalance in the binary problem, sensitivity, but not accuracy, was examined and demonstrated to be superior (89%) to the best individual classifier (CNN with 86.1%). The specificity in the combined classifier decreased from 89.2% to 84%. However, at an equal sensitivity of 89%, the CNN achieved a specificity of only 81.5%
Our findings indicate that the combination of human and artificial intelligence achieves superior results over the independent results of both of these systems.
•This article describes the first experiment on combining human and artificial intelligence for the classification of images suspicious of skin cancer.•The combination achieved a superior accuracy of 82.95% (compared to 81.59%/42.94% achieved by artificial/human intelligence alone).•The combination of human and artificial intelligence indicates superiority over a separated approach.
Common Fundamentals of Psoriasis and Depression Hölsken, Stefanie; Krefting, Frederik; Schedlowski, Manfred ...
Acta dermato-venereologica,
11/2021, Letnik:
101, Številka:
11
Journal Article
Recenzirano
Odprti dostop
Psoriasis is an inflammatory, immune-mediated disease that is frequently associated with psychological comorbidities such as depression. The stigma patients feel because of the appearance of their ...skin may contribute to the high psycho-social burden of psoriasis. However, there is emerging evidence that overlapping biological mechanisms are, to a substantial degree, responsible for the close interaction between psoriasis and depression. Increased proinflammatory mediators, such as C-reactive protein or interleukin-6, are present in both psoriasis and depression, indicating that inflammation may represent a pathophysiological link between the diseases. Anti-inflammatory biologic therapies treat the clinical manifestations of psoriasis, but might also play a significant role in reducing associated depressive symptoms in patients with psoriasis. Comparison between single studies focusing on the change in depressive symptoms in psoriasis is limited by inconsistency in the depression screening tools applied.
Fumaric acid esters (FAEs) remain a widespread therapy option for moderate-to-severe psoriasis. However, drug survival of FAEs is limited by adverse events (AEs) or inadequate treatment response. ...Depressive disturbances are highly prevalent in psoriasis patients and are hypothesized to be associated with the reporting of AEs and therapy discontinuation. This study's aim was to analyze whether psoriasis patients with comorbid depressive symptomatology are more likely to discontinue treatment with FAEs due to AEs and/or inadequate treatment response. Data were retrospectively extracted from the records of patients starting therapy with FAEs in the Department of Dermatology, University Hospital Essen, Germany between 2017 and 2022, covering the first 52 weeks of treatment. Psoriasis severity and depressive symptomatology, as well as AEs and therapy discontinuation, were analyzed. Psoriasis patients (N = 95, 47.37% female) with depressive symptomatology (42.11%) were more likely to discontinue therapy due to patient-reported AEs, while the total number of reported AEs was not associated with depression. The results support the hypothesis that among psoriasis patients with depressive symptoms, the associated introspection and somatization may result in increased sensitivity for AEs and thus in quicker therapy discontinuation. In these patients, the occurrence of nocebo effects should be minimized, e.g. by special communication techniques.
Psoriasis has a strong impact on patients’ lives and is closely linked to psychiatric disorders such as depression, anxiety and substance‐related disorders, especially dependence on alcohol and ...nicotine. The aim of our study was to systematically assess the psychiatric comorbidity and possible associations between psychological factors, disease severity and dermatology‐related quality of life in psoriatic patients from a high‐need university hospital dermatology department. Consecutive psoriatic patients (new and permanent patients) at the Department of Dermatology, University Hospital Essen, Germany, were asked to fill out a paper‐based questionnaire. In the first part of the questionnaire, baseline demographics, pre‐existing mental disorders and data on substance abuse were collected. In the second part of the questionnaire, mental and physical health was explored using different validated self‐rating tests. The current Psoriasis Area and Severity Index (PASI) was documented by a dermatologist. Patients with signs of mental disorders were offered an appointment with a board‐certified psychiatrist. Between August 2016 and February 2019, 228 consecutive psoriatic patients (138 men 60.5%, 90 women 39.5%; mean age, 48.3 years standard deviation, 13.6; range, 18–80) participated in the study. Approximately 50% of the patients had evidence of suffering from mental health problems, mostly depression and anxiety, as well as alcohol dependence. Patients with a PASI of 3 or more showed a statistically significant reduced Dermatology Life Quality Index (DLQI) and a significantly impaired psychological as well as physical quality of life. DLQI correlated with all psychological test results. The data indicate a significant psychological burden in a tertiary psoriatic population. Our findings underscore the importance of screening psoriatic patients for psychiatric disorders, with a focus on depression, anxiety as well as alcohol and nicotine dependence, in a multidimensional approach involving psychiatrists and psychologists.
Visual clinical diagnosis of dermatoses in people of color (PoC) is a considerable challenge in daily clinical practice and a potential cause of misdiagnosis in this patient cohort. The study aimed ...to determine the difference in visual diagnostic skills of dermatologists practicing in Germany in patients with light skin (Ls) and patients with skin of color (SoC) to identify a potential need for further education. From April to June 2023, German dermatologists were invited to complete an online survey with 24 patient photographs depicting 12 skin diseases on both Ls and SoC. The study's primary outcomes were the number of correctly rated photographs and the participants' self-assessed certainty about the suspected visual diagnosis in Ls compared to SoC. The final analysis included surveys from a total of 129 dermatologists (47.8% female, mean age: 39.5 years). Participants were significantly more likely to correctly identify skin diseases by visual diagnostics in patients with Ls than in patients with SoC (72.1% vs. 52.8%, p ≤ 0.001, OR 2.28). Additionally, they expressed higher confidence in their diagnoses for Ls than for SoC (73.9 vs. 61.7, p ≤ 0.001). Therefore, further specialized training seems necessary to improve clinical care of dermatologic patients with SoC.
IntroductionExperimental and clinical data demonstrate that skin diseases like psoriasis are affected by psychological factors and can be modulated by interventions other than conventional drug ...therapy. The expectation of patients towards the benefit of a forthcoming treatment as well as treatment pre-experiences have been demonstrated as crucial factors mediating placebo responses in inflammatory skin diseases. However, it is unknown whether and to what extent treatment outcomes of psoriasis patients under therapy with monoclonal antibodies like secukinumab can be experimentally modulated at subjective and physiological levels by modifying the expectation of patients via verbal instruction or prior experience.Methods and analysisTreatment expectations will be modulated in patients with moderate-to-severe psoriasis undergoing treatment with the anti-interleukin-17A monoclonal antibody secukinumab. Patients with a Psoriasis Area and Severity Index (PASI) >12 will be randomly allocated to one of three groups (N=40 each). As a standard schedule, patients in the pharmacological control group (group 1) will be treated weekly with 300 mg secukinumab, while patients in groups 2 and 3 will receive only 75 mg secukinumab (75% dose reduction) during all treatment weeks. In addition to the injections, patients in group 3 will ingest a novel tasting drink, with a cover story explaining that previous studies showed additional beneficial effects of this combination (drug and drink). Patients will be assessed and treated at nine visits over a 16-week period, during which the severity of pain and itch symptoms, skin lesions and quality of life will be analysed with standardised questionnaires and the PASI.Ethics and disseminationThis study was approved by the Ethics committee of the Medical Faculty of the University Duisburg-Essen. Study outcomes will be published in peer-reviewed scientific journals.
Currently used imaging methods for diagnosis of psoriatic arthritis (PsA) frequently come along with exposure to radiation and can often only show long-term effects of the disease. The aim of the ...study was to check the feasibility of a new optoacoustic imaging method to identify PsA. 22 psoriasis patients and 19 healthy volunteers underwent examination using multispectral optoacoustic tomography (MSOT). The presence of arthritis was assessed via quantification of optoacoustic signal intensity of the endogenous chromophores oxy- and deoxyhemoglobin. We conducted high-resolution real-time ultrasound images of the finger joints. The semi quantitative analysis of the optoacoustic signals for both hemoglobin species showed a significant higher blood content and oxygenation in PsA patients compared to healthy controls.
Our results indicate that MSOT might allow detection of inflammation in an early stage. If the data is further confirmed, this technique might be a suitable tool to avoid delay of diagnosis of PsA.
Background:
Artificial intelligence (AI) 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, with a particular focus on patients with a history of melanoma.
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 toward artificial intelligence in general as well as their attitudes toward 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 individuals (154 with a melanoma diagnosis, 143 without) responded to the questionnaire. About 94% 95% CI = 0.91–0.97 of respondents supported the use of artificial intelligence in medical approaches. 88% 95% CI = 0.85–0.92 would even make their own health data anonymously available for the further development of AI-based applications in medicine. Only 41% 95% CI = 0.35–0.46 of respondents were amenable to the use of artificial intelligence as stand-alone system, 94% 95% CI = 0.92–0.97 to its use as assistance 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 toward the use of artificial intelligence in melanoma diagnostics, especially as an assistance system.
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