What's New in Dermatopathology: Inflammatory Dermatoses

Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends

BACKGROUND

Thanks to the rapid development of computer-based systems and deep-learning-based algorithms, artificial intelligence (AI) has long been integrated into the healthcare field. AI is also particularly helpful in image recognition, surgical assistance and basic research. Due to the unique nature of dermatology, AI-aided dermatological diagnosis based on image recognition has become a modern focus and future trend. Key scientific concepts of review: The use of 3D imaging systems allows clinicians to screen and label skin pigmented lesions and distributed disorders, which can provide an objective assessment and image documentation of lesion sites. Dermatoscopes combined with intelligent software help the dermatologist to easily correlate each close-up image with the corresponding marked lesion in the 3D body map. In addition, AI in the field of prosthetics can assist in the rehabilitation of patients and help to restore limb function after amputation in patients with skin tumors.

THE AIM OF THE STUDY

For the benefit of patients, dermatologists have an obligation to explore the opportunities, risks and limitations of AI applications. This study focuses on the application of emerging AI in dermatology to aid clinical diagnosis and treatment, analyzes the current state of the field and summarizes its future trends and prospects so as to help dermatologists realize the impact of new technological innovations on traditional practices so that they can embrace and use AI-based medical approaches more quickly.

Changing Trends in Dermatopathology Case Complexity: A 9-Year Academic Center Experience

CONTEXT.—

Pathology case volume and complexity impact clinical service burden, staffing, and reimbursement, particularly in an academic setting.

OBJECTIVE.—

To investigate dermatopathology case complexity by using indicators of challenging cases, which require increased clinical service effort.

DESIGN.—

A retrospective review was performed of dermatopathology cases during a 9-year period at a tertiary care academic center. A subset of cases was analyzed for which extractable data were available. Cases requiring the following metrics of complexity were identified: rush processing, consensus agreement, performance of immunohistochemistry, use of special histochemical stains, use of immunofluorescence, examination of additional tissue levels, review of a prior case, addition of an explanatory note, presence of multiple specimen parts, and use of intradepartmental consultation.

RESULTS.—

A total of 8173 cases were reviewed. During the same 3-month period of the year, there was a statistically significant increase in use of rush processing/interpretation, consensus review, number of cases requiring immunostains, special stains, levels, and an explanatory note, and cases reviewed by other subspecialists in the department from 2010 to 2019.

CONCLUSIONS.—

This study shows an increasing trend in dermatopathology case complexity, suggesting that overall clinical service efforts have increased. These findings may inform clinical service staffing and reimbursement.