Adhesion property measurements contribute to a comprehensive
understanding
of the mechanical properties of soft matters. Indentation tests are
a common method for measuring the adhesion force. However, indenters
generally have a large volume and a small sensing angle and, thus,
are not conducive to local detection in high-precision environments.
Here, we propose a vision-based contact adhesion measurement (VisCAM)
method to achieve the contact image and adhesion force on soft matter
surfaces from the perspective of indentation direction. The coupling
of the 7.6 mm diameter probe and a flexible fiber makes the system
similar to a miniaturized endoscope. Classical contact theories and
finite element models are used for the contact mechanics analysis
of silicone rubber. The image grayscale–load mathematical model
is constructed based on the change in contact light spot. Finally,
the uncertainty of the system is less than 4%, and the measurement
error is 0.04 N. In-vitro kidney indentation experiments showed that
the local adhesion force measurement of soft tissues can be completed.
Our method provides better solutions for understanding the adhesion
properties of soft matters.
Lupus erythematosus (LE) has the reputation of being the Great Imitator due to its various cutaneous manifestations, which often lead to difficulties in accurate diagnosis and classification. Even with the assistance of deep learning systems (DLSs), clinical skin image-based single modalities still experience difficulty distinguishing subtypes of LE. Here, we present an application of multimodal DLS (MMDLS), in which the training database consists of clinical skin images, multi-immunohistochemistry (multi-IHC) images and the 2019 European League Against Rheumatism/American College of Rheumatology (EULAR/ACR) scores, to assist in the classification of LE subtypes (discoid LE (DLE), annular subacute cutaneous LE (A-SCLE), papulosquamous subacute cutaneous LE (P-SCLE), systemic LE (SLE)) and to aid in the differential diagnosis of other similar skin diseases. A total of 386 cases with 580 clinical skin images, 3380 multi-IHC images and 2019 EULAR/ACR scores from 25 institutions in China were included, and EfficientNet-B3 and ResNet-18 were utilized in this study. By comparing the single-modal and dual-modal DLS performances in multiple classifications, we demonstrate the superiority of the MMDLS (Ave-Sen = 0.9116, Ave-Spe = 0.9921, Ave-Pre = 0.9281). Moreover, the performance of MMDLS on 13 classifications was superior to that of dermatologists and pathologists (F1 score: dermatologists = 0.4471, pathologists = 0.6691, MMDLS = 0.9582) with an average area under the curve (AUC) of 0.9956. These results highlight the potential of the combined application of MMDLS and multi-IHC images to assist pathologists and dermatologists in diagnosing LE subtypes and similar skin diseases.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.