High Frequency of Osteophytes Detected by High-Resolution Ultrasound at the Finger Joints of Asymptomatic Factory Workers.

Hand Osteoarthritis (HOA) is a frequently occurring musculoskeletal disease that impacts health. Diagnostic criteria often incorporate osteophytes documented through imaging procedures. Radiographic imaging is considered the gold standard; however, more sensitive and safer methods like ultrasound imaging are becoming increasingly important. We conducted a population-based cross-sectional study to examine the prevalence, grade, and pattern of osteophytes using high-resolution ultrasound investigation. Factory workers were recruited on-site for the study. Each participant had 26 finger joints examined using ultrasonography to grade the occurrence of osteophytes on a semi-quantitative scale ranging from 0-3, where higher scores indicate larger osteophytes. A total of 427 participants (mean age 53.5 years, range 20-79 years) were included, resulting in 11,000 joints scored. At least one osteophyte was found in 4546 out of 11,000 (41.3%) joints or in 426 out of 427 (99.8%) participants, but only 5.0% (553) of the joints showed grade 2 or 3 osteophytes. The total osteophyte sum score increased by 0.18 per year as age increased (p < 0.001). The distal interphalangeal joints were the most commonly affected, with 61%, followed by the proximal interphalangeal joints with 48%, carpometacarpal joint 1 with 39%, and metacarpophalangeal joints with 16%. There was no observed impact of gender or workload. In conclusion, ultrasound imaging proves to be a practical screening tool for osteophytes and HOA. Grade 1 osteophytes are often detected in the working population through ultrasound assessments and their incidence increases with age. The occurrence of grade 2 or 3 osteophytes is less frequent and indicates the clinical presence of HOA. Subsequent evaluations are imperative to ascertain the predictive significance of early osteophytes.

Therapeutic application of machine learning in psoriasis: A Prisma systematic review.

Dermatology, being a predominantly visual-based diagnostic field, has found itself to be at the epitome of artificial intelligence (AI)-based advances. Machine learning (ML), a subset of AI, goes a step further by recognizing patterns from data and teaches machines to automatically learn tasks. Although artificial intelligence in dermatology is mostly developed in melanoma and skin cancer diagnosis, advances in AI and ML have gone far ahead and found its application in ulcer assessment, psoriasis, atopic dermatitis, onychomycosis, etc. This article is focused on the application of ML in the therapeutic aspect of psoriasis.

Surgical Treatment in Hidradenitis Suppurativa.

Hidradenitis suppurativa (HS) is a chronic, progressive inflammatory disorder of follicular occlusion with pubertal onset that presents as painful inflammatory nodules, sinus tracts, and tunnelling in apocrine-gland-rich areas, such as the axilla, groin, lower back, and buttocks. The disease course is complicated by contractures, keloids, and immobility and is often associated with a low quality of life. It is considered a disorder of follicular occlusion with secondary inflammation, though the exact cause is not known. Management can often be unsatisfactory and challenging due to the chronic nature of the disease and its adverse impact on the quality of life. A multidisciplinary approach is key to prompt optimal disease control. The early stages can be managed with medical treatment, but the advanced stages most likely require surgical intervention. Various surgical options are available, depending upon disease severity and patient preference. In this review an evidence-based outline of surgical options for the treatment of HS are discussed. Case reports, case series, cohort studies, case-control studies, and Randomized Clinical Trials (RCT)s available in medical databases regarding surgical options used in the treatment of HS were considered for the review presented in a narrative manner in this article.

Musculoskeletal ultrasound as a screening-tool for rheumatoid arthritis: results of the "Rheuma-Truck" screening and awareness initiative.

OBJECTIVE: To evaluate musculoskeletal ultrasound (MSUS) as a screening tool for rheumatoid arthritis (RA) and osteoarthritis (OA) patients in a rheumatology-screening program. PATIENTS AND METHODS: To raise awareness for rheumatic diseases, a mobile rheumatology office was deployed in different cities of Germany ("Rheuma-Truck"). Standardized questionnaire assessment, testing for rheumatoid factor and citrullinated peptide antibodies and medical student driven MSUS of the clinically dominant hand/foot including wrist, MCP-II, -III, -V, PIP-II, -III, MTP-II and -V were offered free of charge to the population. In case of suspicious results, a rheumatologist was consulted. RESULTS: In MSUS, 192 of 560 selected volunteers (aged 18-89, mean 52.7 years; 72.9% female) had suspicious findings including synovitis or erosions primarily affecting the MTP-II (11.8%), dorsal wrist (8.9%), and MCP-II (7%). 354 of the 560 volunteers further visited a rheumatologist of whom 76 were diagnosed with RA. According to the 'US7 Score', a sum scores ≥ 5 was significantly predictive for RA (odds ratio (OR) 5.06; confidence interval (CI) 0.83-35.32). 313 volunteers displayed signs of OA including osteophytes, while MCP-II (36.2%), MCP-III (14.8%), and the wrist (10.5%) were mostly affected. Diagnosis of RA was favoured over OA if the wrist (OR 4.2; CI 1.28-13.95), MTP-II (OR 1.62; CI 1.0-2.6), and MCP-V (OR 2.0; CI 1.0-3.8) were involved. CONCLUSION: Medical student driven MSUS by the 'US7 Score' can facilitate diagnosis of RA in rheumatology-screening programs due to the level of the score and the affected joints. A high rate of unknown OA signs was detected by MSUS. A mobile rheumatology office displays an opportunity to screen patients for RA and OA.

Musculoskeletal ultrasound as a screening-tool for rheumatoid arthritis: results of the "Rheuma-Truck" screening and awareness initiative

Abstract Objective: To evaluate musculoskeletal ultrasound (MSUS) as a screening tool for rheumatoid arthritis (RA) and osteoarthritis (OA) patients in a rheumatology-screening program. Patients and methods: To raise awareness for rheumatic diseases, a mobile rheumatology office was deployed in different cities of Germany ("Rheuma-Truck"). Standardized questionnaire assessment, testing for rheumatoid factor and citrullinated peptide antibodies and medical student driven MSUS of the clinically dominant hand/foot including wrist, MCP-II, -III, -V, PIP-II, -III, MTP-II and -V were offered free of charge to the population. In case of suspicious results, a rheumatologist was consulted. Results: In MSUS, 192 of 560 selected volunteers (aged 18-89, mean 52.7 years; 72.9% female) had suspicious findings including synovitis or erosions primarily affecting the MTP-II (11.8%), dorsal wrist (8.9%), and MCP-II (7%). 354 of the 560 volunteers further visited a rheumatologist of whom 76 were diagnosed with RA. According to the 'US7 Score', a sum scores ≥ 5 was significantly predictive for RA (odds ratio (OR) 5.06; confidence interval (CI) 0.83-35.32). 313 volunteers displayed signs of OA including osteophytes, while MCP-II (36.2%), MCP-III (14.8%), and the wrist (10.5%) were mostly affected. Diagnosis of RA was favoured over OA if the wrist (OR 4.2; CI 1.28-13.95), MTP-II (OR 1.62; CI 1.0-2.6), and MCP-V (OR 2.0; CI 1.0-3.8) were involved. Conclusion: Medical student driven MSUS by the 'US7 Score' can facilitate diagnosis of RA in rheumatology-screening programs due to the level of the score and the affected joints. A high rate of unknown OA signs was detected by MSUS. A mobile rheumatology office displays an opportunity to screen patients for RA and OA.

Machine Learning and Its Application in Skin Cancer.

Artificial intelligence (AI) has wide applications in healthcare, including dermatology. Machine learning (ML) is a subfield of AI involving statistical models and algorithms that can progressively learn from data to predict the characteristics of new samples and perform a desired task. Although it has a significant role in the detection of skin cancer, dermatology skill lags behind radiology in terms of AI acceptance. With continuous spread, use, and emerging technologies, AI is becoming more widely available even to the general population. AI can be of use for the early detection of skin cancer. For example, the use of deep convolutional neural networks can help to develop a system to evaluate images of the skin to diagnose skin cancer. Early detection is key for the effective treatment and better outcomes of skin cancer. Specialists can accurately diagnose the cancer, however, considering their limited numbers, there is a need to develop automated systems that can diagnose the disease efficiently to save lives and reduce health and financial burdens on the patients. ML can be of significant use in this regard. In this article, we discuss the fundamentals of ML and its potential in assisting the diagnosis of skin cancer.

Increased risk of angiosarcoma secondary to cancer radiotherapy: Case series and review of the treatment options.

Angiosarcomas (ASs) are aggressive tumors of vascular endothelial origin, occurring sporadically or in association with prior radiotherapy or chronic lymphedema. With only 1-5% of all sarcomas, the incidence seems low, but for the affected patient due to the extremely poor prognosis and the limited treatment options, the fate is often inevitable. Radiotherapy, chemotherapy, or "target therapy" have been used in the management of AS, but represent individual case decisions without lasting evidence. Over the past few years, breast-conserving surgery followed by radiation therapy, known as breast-conserving therapy (BCT), is being employed as a standard treatment for early-stage breast cancer, but there has been an increase in reports of AS following BCT. We report two cases of AS following BCT and one case of primary AS involving the lower limb.