Augmented Decision-Making in wound Care: Evaluating the clinical utility of a Deep-Learning model for pressure injury staging.

BACKGROUND: Precise categorization of pressure injury (PI) stages is critical in determining the appropriate treatment for wound care. However, the expertise necessary for PI staging is frequently unavailable in residential care settings. OBJECTIVE: This study aimed to develop a convolutional neural network (CNN) model for classifying PIs and investigate whether its implementation can allow physicians to make better decisions for PI staging. METHODS: Using 3,098 clinical images (2,614 and 484 from internal and external datasets, respectively), a CNN was trained and validated to classify PIs and other related dermatoses. A two-part survey was conducted with 24 dermatology residents, ward nurses, and medical students to determine whether the implementation of the CNN improved initial PI classification decisions. RESULTS: The top-1 accuracy of the model was 0.793 (95% confidence interval [CI], 0.778-0.808) and 0.717 (95% CI, 0.676-0.758) over the internal and external testing sets, respectively. The accuracy of PI staging among participants was 0.501 (95% CI, 0.487-0.515) in Part I, improving by 17.1% to 0.672 (95% CI, 0.660-0.684) in Part II. Furthermore, the concordance between participants increased significantly with the use of the CNN model, with Fleiss' κ of 0.414 (95% CI, 0.410-0.417) and 0.641 (95% CI, 0.638-0.644) in Parts I and II, respectively. CONCLUSIONS: The proposed CNN model can help classify PIs and relevant dermatoses. In addition, augmented decision-making can improve consultation accuracy while ensuring concordance between the clinical decisions made by a diverse group of health professionals.

Unconventional Molecular Design Approach of High-Efficiency Deep Blue Thermally Activated Delayed Fluorescent Emitters Using Indolocarbazole as an Acceptor.

Unconventional blue thermally activated delayed fluorescent emitters having electron-donating type indolocarbazole as an acceptor were developed by attaching carbazolylcarbazole or acridine donors to the indolocarbazole acceptor. Three compounds were derived from the indolocarbazole acceptor. The indolocarbazole-acridine combined products showed efficient delayed fluorescent behavior and a high quantum efficiency of 19.5% with a color coordinate of (0.15, 0.16) when they were evaluated as thermally activated delayed fluorescent emitters in deep blue fluorescent devices. This is the first demonstration of the use of electron-donating carbazole-derived moieties as efficient acceptor units of blue thermally activated delayed fluorescent emitters.

Molecular Orbital Controlling Donor Moiety for High-Efficiency Thermally Activated Delayed Fluorescent Emitters.

A new donor moiety, 7,7,13,13-tetramethyl-7,13-dihydro-5H-indeno[1,2-b]acridine (IAc), was developed to control the highest occupied molecular orbital (HOMO) dispersion of thermally activated delayed fluorescent (TADF) emitters. The IAc unit expanded the HOMO dispersion of the emitters and increased the quantum efficiency of the TADF devices up to 20.9 %.

Carboline derivatives with an ortho-linked terphenyl core for high quantum efficiency in blue phosphorescent organic light-emitting diodes.

Two host materials, 9-(3''-(9H-carbazol-9-yl)-[1,1':2',1''-terphenyl]-3-yl)-α-carboline (CzOTCb) and 3,3''-bis(α-carbolin-9-yl)-1,1':2',1''-terphenyl (CbOTCb), derived from carboline and ortho-linked terphenyl were synthesized as high triplet energy materials and showed a high triplet energy of 2.90 eV. CzOTCb and CbOTCb were evaluated as the host materials for blue phosphorescent organic light-emitting diodes and high quantum efficiencies of 27.4% and 28.8% were obtained using the CzOTCb and CbOTCb hosts, respectively.