Support vector machines (SVMs) are well-known machine learning algorithms for classification and regression applications. In the healthcare domain, they have been used for a variety of tasks including diagnosis, prognosis, and prediction of disease outcomes. This review is an extensive survey on the current state-of-the-art of SVMs developed and applied in the medical field over the years. Many variants of SVM-based approaches have been developed to enhance their generalisation capabilities. We illustrate the most interesting SVM-based models that have been developed and applied in healthcare to improve performance metrics on benchmark datasets, including hybrid classification methods that combine, for instance, optimization algorithms with SVMs. We even report interesting results found in medical applications related to real-world data. Several issues around SVMs, such as selection of hyperparameters and learning from data of questionable quality, are discussed as well. The several variants developed and introduced over the years could be useful in designing new methods to improve performance in critical fields such as healthcare, where accuracy, specificity, and other metrics are crucial. Finally, current research trends and future directions are underlined.

Objective. To evaluate in SSc, the frequency of digital lesions and the morphology, characteristics, natural course and time to healing of 1614 digital ulcers (DUs). Methods. One hundred SSc patients were followed up for 4 years. In the first step, the digital lesions were observed and classified at the time of presentation [digital pitting scar (DPS); DU; calcinosis; gangrene]. In the second step, DUs were divided into subsets according to their origin and main features. In the third step, the time to healing was recorded for each DU and the influence of DU main characteristics on time to healing was also evaluated. Results. In the first step, 1614 digital lesions were observed: DPS, 712 (44.1%) lesions; DU, 785 (48.6%); calcinosis, 110 (6.8%); and gangrene, 7 (0.8%). In the second step, DUs were subsetted as follows: DU developed on DPS (8.8%), pure DU; DU developed on calcinosis (60%); DU derived from gangrene. In the third step, the mean time to healing was 25.6 (15.6) days in DPS, 76.2 (64) days in pure DU, 93.6 (59.2) days in calcinosis ulcers and 281.1 (263.3) in gangrene. Conclusions. In SSc, digital lesions are represented by DPS, DU, calcinosis and gangrene, and provide an evidence-based DU subsetting according to their origin and main characteristics. Subsetting may be helpful for a precise DU evaluation and staging, and in randomized controlled trials for a precise identification of those DUs that are to be included in therapeutic studies.

Background Currently, assessment of dermal thickness in systemic sclerosis (SSc) is performed by palpation and assessment using the modified Rodnan skin score (mRSS). Objective To verify whether high frequency ultrasound (US) may be a reliable and a reproducible method to measure digital dermal thickness. Methods In 70 patients with SSc, skin thickness was evaluated with US by 2 observers at 2 different sites on the second digit of the dominant limb to determine the interobserver variability. Patients and controls were examined twice by the first observer for intraobserver variability. Patients were divided into three subgroups according to the phase of the disease (oedematous, fibrotic or atrophic). Results At both examined areas, US showed a significant dermal thickening (p<0.001) in the whole group of patients with SSc. A low intraobserver and interobserver variability was found. A highly significant correlation between the global mRSS and the local dermal thickness at the two examined sites (p=0.032, p=0.021) was detected. Skin thickness was significantly higher in the oedematous than in the fibrotic group (p<0.001) and significantly higher in the fibrotic and the oedematous group (p<0.001) than in the atrophic group (p<0.002). Conclusions US is a reliable tool giving reproducible results, and is able to detect digital dermal thickening in SSc.