Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Accurate assessment of the percentage of total body surface area (%TBSA) of burn wounds is crucial in the management of burn patients. The resuscitation fluid and nutritional needs of burn patients, their need for intensive unit care, and probability of mortality are all directly related to %TBSA. It is difficult to estimate a burn area of irregular shape by inspection. Many articles have reported discrepancy in estimating %TBSA by different doctors. Objective: We propose a method, based on deep learning, for burn wound detection, segmentation and calculation of % TBSA on a pixel-to-pixel basis. Methods: A two-step procedure was used to convert burn wound diagnosis into %TBSA. In the first step, images of burn wounds were collected and labeled by burn surgeons and the dataset was then input into two deep learning architectures, U-Net and Mask R-CNN, each configured with two different backbones, to segment the burn wounds. In the second step, we collected and labeled images of hands to create another dataset, which was also input into U-Net and Mask R-CNN to segment the hands. The percentage of TBSA of the burn wounds was then calculated by comparing the pixels of mask areas on the images of the burn wound and hand of the same patient according to the rule of hand, which says that one’s hand accounts for 0.8% of TBSA. Results: A total of 2591 images of burn wounds were collected and labeled to form the burn-wound dataset. The dataset was randomly split into a ratio of 8:1:1 to form the training, validation, and testing sets. Four hundred images of volar hands were collected and labeled to form the hand dataset, which was also split into three sets using the same method. For the images of burn wounds, Mask R-CNN with ResNet101 had the best segmentation result with a Dice coefficient (DC) of 0.9496, while U-Net with ResNet101 had a DC of 0.8545. For the hand images, U-Net and Mask R-CNN had similar performance with a DC of 0.9920 and 0.9910, respectively. Lastly, we conducted a test diagnosis in a burn patient. Mask R-CNN with ResNet-101 had on average less deviation (0.115% TBSA) from the ground truth than burn surgeons. Conclusions: This is one of the first studies to diagnose all depths of burn wounds and convert the segmentation results into %TBSA using different deep learning models. We aimed to assist medical staff in estimating burn size more accurately and thereby helping to provide precise care to burn victims.

Significance: With the increasing diabetic population worldwide, diabetic foot ulcers are a significant concern. This study aimed to compare the efficacy of skin substitutes, biomaterials, and topical agents with standard care. Recent Advances: A meta-analysis was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed, EMBASE, and Web of Science were searched using the following keywords: diabetes mellitus AND skin graft OR tissue replacement OR dressing OR drug. Two independent reviewers performed data collection and quality assessment of the eligible studies. The primary outcome was the 12–16-week healing rates and the secondary outcome was recurrence rates. Critical Issues: Thirty-eight randomized controlled trials, including 3862 patients, were analyzed. The studies exhibited low heterogeneity (τ2 = 0.10) without significant asymmetry (Egger’s test, p = 0.8852). After pooling direct and indirect estimates, placenta-based tissue products exhibited the best wound healing probability (P-score = 0.90), followed by skin substitutes with living cells (P-score = 0.70), acellular skin substitutes (P-score = 0.56), and advanced topical dressings (P-score = 0.34) compared with standard of care. The recurrence analysis showed significant improvement in the intervention group compared with the control group (11.21% vs. 15.15%). Future Directions: This network meta-analysis provides the relative effectiveness and rank of biomaterials and topical dressings in diabetic foot ulcer healing. The results could help clinical decision making. Keywords: Diabetic Foot Ulcer, Skin Substitute, Biological Dressing, Wound Healing

Background: Few studies have addressed patient preferences in emergent surgical decision making. Aim of the study: Analyzing patient preferences for hand trauma reconstruction to propose a decision-making model. Methods: A conjoint analysis survey was developed with Sawtooth Software. Three common flaps—i.e., a cross-finger flap (CFF), a dorsal metacarpal artery perforator flap (DMAPF), and an arterialized venous flap (AVF)—were listed as treatment alternatives. Five attributes corresponding to these flaps were recovery time, total procedure, postoperative care methods, postoperative scar condition, and complication rate. Utility and importance scores were generated from the software, and preference characteristics were evaluated using cluster analysis. Results: The survey was completed by 197 participants with hand trauma. Complication risk received the highest importance score (42.87%), followed by scar condition (21.55%). Cluster analysis classified the participants as “conservative,” “practical,” and “dual-concern”. The dual-concern and conservative groups had more foreign laborers and highly educated participants, respectively, than the other groups. Most participants in the conservative and practical groups preferred DMAPF, whereas those in the dual-concern group favored CFF. Our proposed model consisted of shared decision-making and treatment recommendation pathways. Conclusion: Incorporating patient preferences into the decision-making model can strengthen patient-centered care. Further research on the applications of the proposed model is warranted.

Mandibular defects may result from tumor ablations, trauma, or radiation necrosis. Significant segmental mandibular loss or hemimandibular loss may sometimes be replaced with mandibular implants by ENT surgeons/oral surgeons/head and neck surgeons. However, this may bring about mandibular implant failure in long-term follow-up. Mandibular implant failures usually manifest as: soft tissue atrophy, mandibular implant extrusion, infection, facial nerve involvement, facial asymmetry, derangement of occlusion and mastication, orocutaneous fistula, etc. Over 30 years, the authors have treated 102 patients with mandibular implant failure. Reconstruction may involve removal of the mandibular implant and immediate replacement of the mandibular defect with a piece of vascularized bone flap, not only to compensate for bone loss but also to replace neighboring soft tissue and possible skin defects. Frequently used flaps have been vascularized iliac bone (89/102) or vascularized fibula grafts (13/102). During follow-up, iliac bone flap reconstruction has yielded more favorable results due to its ample bone bulk and adequate soft tissue coverage. Fibula flaps with osteotomies have been associated with an increasing incidence of malunion/nonunion and subsequent easy deformation.

Background and Objectives: Autologous fat transplantation using fat alone can lead to postoperative complications. Laminin–alginate beads have been investigated as carriers for adipose mesenchymal stem cells (ASCs), with the potential to modulate cell release kinetics, influence macrophage responses, and thereby affect the maintenance of graft volume. We hypothesized that encapsulating ASCs in laminin–alginate beads would allow gradual ASC release into the transplantation site and influence the macrophage-related microenvironment during fat graft maturation. Materials and Methods: ASCs were isolated from two female enhanced green fluorescent protein (eGFP) transgenic rats. For SC (ASC + fat) grafts, ASCs were freely mixed with 1 mL of mature adipose tissue. For bead-encapsulated cell (BEC) (laminin–alginate + ASC + fat) grafts, ASCs were first encapsulated in laminin–alginate beads and subsequently mixed with 1 mL of fat tissue. The fat graft was subcutaneously injected into the backs of the rats, which were subsequently euthanized at weeks 4, 6, and 8. The grafts were then harvested for histological and immunofluorescence analyses. Results: The histological analysis indicated that the number of eGFP-ASCs in the BEC grafts gradually increased between weeks 4 and 8, whereas that in the SC grafts decreased over the same period. The BEC grafts also exhibited a numerically higher degree of M2 macrophage infiltration. Conclusions: Laminin–alginate bead encapsulation was associated with prolonged persistence of eGFP-ASCs within the graft area. Although the macrophage-related findings were not statistically significant, they suggest a potential interaction between gradual ASC release and the local graft microenvironment.