3D surface roughness measurement for scaliness scoring of psoriasis lesions

Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases

Immuno-correlated dermatological pathologies refer to skin disorders that are closely associated with immune system dysfunction or abnormal immune responses. Advancements in the field of artificial intelligence (AI) have shown promise in enhancing the diagnosis, management, and assessment of immuno-correlated dermatological pathologies. This intersection of dermatology and immunology plays a pivotal role in comprehending and addressing complex skin disorders with immune system involvement. The paper explores the knowledge known so far and the evolution and achievements of AI in diagnosis; discusses segmentation and the classification of medical images; and reviews existing challenges, in immunological-related skin diseases. From our review, the role of AI has emerged, especially in the analysis of images for both diagnostic and severity assessment purposes. Furthermore, the possibility of predicting patients' response to therapies is emerging, in order to create tailored therapies.

Factors Associated With Ultrasound Color Doppler Twinkling by Breast Biopsy Markers: In Vitro and Ex Vivo Evaluation of 35 Commercially Available Markers

BACKGROUND. Targeted axillary lymph node dissection after neoadjuvant systemic therapy (NST) for breast cancer depends on identifying marked metastatic lymph nodes. However, ultrasound visualization of biopsy markers is challenging. OBJECTIVE. The purpose of our study was to identify biopsy markers that show actionable twinkling in cadaveric breast and to assess the association of actionable twinkling with markers' surface roughness. METHODS. Commercial breast biopsy markers were evaluated for twinkling artifact in various experimental conditions relating to scanning medium (solid gel phantom, ultrasound coupling gel, cadaveric breast), transducer (ML6-15, 9L, C1-6), and embedding material (present vs absent). Markers were assigned twinkling scores from 0 (confident in no twinkling) to 4 (confident in exuberant twinkling); a score of 3 or greater represented actionable twinkling (sufficient confidence to rely solely on twinkling for target localization). Markers were hierarchically advanced to evaluation with increasingly complex media if showing at least minimal twinkling for a given medium. A 3D coherence optical profiler measured marker surface roughness. Mixed-effects proportional odds regression models assessed associations between twinkling scores and transducer and embedding material; Wilcoxon rank sum test evaluated associations between actionable twinkling and surface roughness. RESULTS. Thirty-five markers (21 with embedding material) were evaluated. Ten markers without embedding material advanced to evaluation in cadaveric breast. Higher twinkling scores were associated with presence of embedding material (odds ratio [OR] = 5.05 in solid gel phantom, 9.84 in coupling gel) and transducer (using the C1-6 transducer as reference; 9L transducer: OR = 0.36, 0.83, and 0.04 in solid gel phantom, ultrasound coupling gel, and cadaveric breast; ML6-15 transducer: OR = 0.07, 0.18, and 0.00 respectively; post hoc p between 9L and ML6-15: p < .001, p = .02, and p = .04). In cadaveric breast, three markers (Cork, Professional Q, MRI [Flex]) exhibited actionable twinkling for two or more transducers; surface roughness was significantly higher for markers with than without actionable twinkling for C1-6 (median values: 0.97 vs 0.35, p = .02) and 9L (1.75 vs 0.36; p = .002) transducers. CONCLUSION. Certain breast biopsy markers exhibited actionable twinkling in cadaveric breast. Twinkling was observed with greater confidence for the C1-6 and 9L transducers than the ML6-15 transducer. Actionable twinkling was associated with higher marker surface roughness. CLINICAL IMPACT. Use of twinkling for marker detection could impact preoperative or intraoperative localization after NST.

Twinkling-guided ultrasound detection of polymethyl methacrylate as a potential breast biopsy marker: a comparative investigation

Since its first description 25 years ago, color Doppler twinkling has been a compelling ultrasound feature in diagnosing urinary stones. While the fundamental cause of twinkling remains elusive, the distinctive twinkling signature is diagnostically valuable in clinical practice. It can be inferred that if an entity twinkles, it empirically has certain physical features. This work investigates a manipulable polymeric material, polymethyl methacrylate (PMMA), which twinkles and has measurable surface roughness and porosity that likely contribute to twinkling. Comparative investigation of these structural properties and of the twinkling signatures of breast biopsy markers made from PMMA and selected commercially available markers showed how twinkling can improve ultrasound detection of devices intentionally designed to twinkle. While this specific application of detecting breast biopsy markers by twinkling may provide a way to approach an unmet need in the care of patients with breast cancer, this work ultimately provides a platform from which the keys to unlocking the fundamental physics of twinkling can be rigorously explored.

Using Ultrasound Color Doppler Twinkling to Identify Biopsy Markers in the Breast and Axilla

In breast radiology, ultrasound detection of biopsy markers or clips for localization purposes is often challenging, especially in the axilla. The purpose of this research was to test the hypothesis that the surface roughness of biopsy clips would elicit a twinkling signature on color Doppler, making them more readily identifiable by ultrasound. Ultrasound color Doppler imaging of 12 biopsy markers was performed and consensus scoring of the degree of twinkling (0 [no twinkling] to 4 [exuberant twinkling]) was obtained for each of the markers. The surface roughness characteristics of the markers were measured using 3-D coherence scanning interferometry. The 3 markers scoring at least 3 for twinkling in vitro were cork, Q and Vision. Of these 3 markers, only the cork marker scored a 4 ex vivo and in cadaveric tissue. Surface roughness metrics demonstrated a positive estimated correlation with the twinkling scores (rho = 0.33, 95% CI = [-0.48 to 0.84]). Of the 12 markers tested, the markers that twinkled corresponded to surface roughness measured with non-contact 3-D optical imaging. Qualitatively, lower color scales and color frequencies optimized twinkling, but the most specific qualitative predictor of confidence in twinkling was insensitivity to changes in color scale and color frequency values.

Scientific validation of three-dimensional stereophotogrammetry compared to the IGAIS clinical scale for assessing wrinkles and scars after laser treatment

Measuring outcomes from treatments to the skin is either reliant upon patient’s subjective feedback or scale-based peer assessments. Three-Dimensional stereophotogrammetry intend to accurately quantify skin microtopography before and after treatments. The objective of this study is comparing the accuracy of stereophotogrammetry with a scale-based peer evaluation in assessing topographical changes to skin surface following laser treatment. A 3D stereophotogrammetry system photographed skin surface of 48 patients with facial wrinkles or scars before and three months after laser resurfacing, followed immediately by topical application of vitamin C. The software measured changes in skin roughness, wrinkle depth and scar volume. Images were presented to three observers, each independently scoring cutaneous improvement according to Investigator Global Aesthetic Improvement Scale (IGAIS). As for the results, a trend reflecting skin/scar improvement was reported by 3D SPM measurements and raters. The percentage of topographical change given by the raters matched 3D SPM findings. Agreement was highest when observers analysed 3D images. However, observers overestimated skin improvement in a nontreatment control whilst 3D SPM was precise in detecting absence of intervention. This study confirmed a direct correlation between the IGAIS clinical scale and 3D SPM and confirmed the efficacy and accuracy of the latter when assessing cutaneous microtopography alterations as a response to laser treatment.

Machine Learning Applications in the Evaluation and Management of Psoriasis: A Systematic Review

Background

Machine learning (ML), a subset of artificial intelligence (AI) that aims to teach machines to automatically learn tasks by inferring patterns from data, holds significant promise to aid psoriasis care. Applications include evaluation of skin images for screening and diagnosis as well as clinical management including treatment and complication prediction.

Objective

To summarize literature on ML applications to psoriasis evaluation and management and to discuss challenges and opportunities for future advances.

Methods

We searched MEDLINE, Google Scholar, ACM Digital Library, and IEEE Xplore for peer-reviewed publications published in English through December 1, 2019. Our search queries identified publications with any of the 10 computing-related keywords and "psoriasis" in the title and/or abstract.

Results

Thirty-three studies were identified. Articles were organized by topic and synthesized as evaluation- or management-focused articles covering 5 content categories: (A) Evaluation using skin images: (1) identification and differential diagnosis of psoriasis lesions, (2) lesion segmentation, and (3) lesion severity and area scoring; (B) clinical management: (1) prediction of complications and (2) treatment.

Conclusion

Machine learning has significant potential to aid psoriasis evaluation and management. Current topics popular in ML research on psoriasis are the evaluation of medical images, prediction of complications, and treatment discovery. For patients to derive the greatest benefit from ML advancements, it is helpful for dermatologists to have an understanding of ML and how it can effectively aid their assessments and decision-making.

A Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection

BACKGROUND

Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival.

DISCUSSION

This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection.

CONCLUSION

This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.