A Novel, Deep Learning-Based, Automatic Photometric Analysis Software for Breast Aesthetic Scoring.

Background Breast aesthetics evaluation often relies on subjective assessments, leading to the need for objective, automated tools. We developed the Seoul Breast Esthetic Scoring Tool (S-BEST), a photometric analysis software that utilizes a DenseNet-264 deep learning model to automatically evaluate breast landmarks and asymmetry indices. Methods S-BEST was trained on a dataset of frontal breast photographs annotated with 30 specific landmarks, divided into an 80-20 training-validation split. The software requires the distances of sternal notch to nipple or nipple-to-nipple as input and performs image preprocessing steps, including ratio correction and 8-bit normalization. Breast asymmetry indices and centimeter-based measurements are provided as the output. The accuracy of S-BEST was validated using a paired t -test and Bland-Altman plots, comparing its measurements to those obtained from physical examinations of 100 females diagnosed with breast cancer. Results S-BEST demonstrated high accuracy in automatic landmark localization, with most distances showing no statistically significant difference compared with physical measurements. However, the nipple to inframammary fold distance showed a significant bias, with a coefficient of determination ranging from 0.3787 to 0.4234 for the left and right sides, respectively. Conclusion S-BEST provides a fast, reliable, and automated approach for breast aesthetic evaluation based on 2D frontal photographs. While limited by its inability to capture volumetric attributes or multiple viewpoints, it serves as an accessible tool for both clinical and research applications.

Aneurysmal bone cyst arising from the surgically removed craniofacial fibrous dysplasia in the long-term follow-up: a case report.

Fibrous dysplasia (FD) is a rare skeletal disorder characterized by abnormal fibro-osseous connective tissue replacing normal bone. Despite its benign behavior, craniofacial FD can cause morphological disfigurement, headache, and even blindness as a result of the produced mass effect. Surgical resection is recommended when the patient shows apparent clinical symptoms or aggravating facial asymmetry. Postoperative complications have been reported, such as hematoma, surgical site infection, abscess formation, resorption of the bone graft used for reconstruction, and recurrence. An aneurysmal bone cyst (ABC) is a rare benign bony lesion that can occur secondary to preexisting bone tumor. Secondary ABCs in craniofacial FD are extremely rare in the literature, accounting for less than 30, all of which are either case reports or series. We report an extremely rare case of symptomatic secondary ABC arising from craniofacial FD that had been misdiagnosed with abscess formation or recurrence and was surgically removed. Notably, 17 years elapsed between the primary surgery and the complication of secondary ABC. The patient underwent total removal of secondary ABC. After surgery, symptoms were relieved, with no recurrence observed during a 6-month follow-up.

Negative Pressure Wound Therapy (NPWT) after Hybrid Reconstruction of Occipital Pressure Sore Using Local Flap and Skin Graft.

Background and objectives: Pressure sores are a common medical burden among patients, particularly those who are bedridden or frail. Surgical management of occipital pressure sores poses unique challenges due to limited elasticity and the spherical shape of the scalp. This study aims to evaluate the efficacy and safety of a novel reconstruction method utilizing a local transpositional flap and split-thickness skin graft with negative pressure wound therapy (NPWT) for occipital pressure sore treatment. Material and methods: A retrospective analysis was performed on patients with occipital pressure sores who underwent hybrid reconstructions using a local flap and split-thickness skin graft in conjunction with NPWT. Surgical outcomes, including flap survival rate, graft take percentage, and complications, were assessed. A comparative analysis was performed between the NPWT group and the conventional dressing group. Results: The NPWT group (n = 24) demonstrated a significantly higher mean graft take percentage at postoperative day 14 compared with the conventional dressing group (n = 22) (98.2% vs. 81.2%, p < 0.05). No significant difference in flap survival rate was observed between the two groups. Conclusions: As the aging population continues to grow, occipital pressure sores have gained significant attention as a crucial medical condition. The innovative surgical method incorporating NPWT offers an efficient and safe treatment option for patients with occipital pressure sores, potentially establishing itself as the future gold standard for managing this condition.

Revisit to functional data analysis of sleeping energy expenditure.

In this paper, we consider the classification problem of functional data including the sleeping energy expenditure (SEE) data, focusing on functional classification. Many existing classification rules are not effective in distinguishing the two classes of SEE data, because the trajectories of each observation have very different patterns for each class. It is often observed that some aspect of data such as the variability of paths is helpful in classification of functional data. To reflect this issue, we introduce a variable measuring the length of path in functional data and then propose a logistic model with fused lasso that considers the behavior of fluctuation of path as well as local correlations within each path. Our proposed model shows a significant improvement over some models used in the existing literature on the classification accuracy rate of functional data such as SEE data. We carry out simulation studies to show the finite sample performance and the gain that it makes in comparison with fused lasso without considering path length. With two more real datasets studied in some existing literature, we demonstrate that the new model achieves better or similar accuracy rate than the best accuracy rates reported in those studies.

Clinical evaluation of non-contact infrared thermometers.

Non-contact infrared thermometers (NCITs) are being widely used during the COVID-19 pandemic as a temperature-measurement tool for screening and isolating patients in healthcare settings, travelers at ports of entry, and the general public. To understand the accuracy of NCITs, a clinical study was conducted with 1113 adult subjects using six different commercially available NCIT models. A total of 60 NCITs were tested with 10 units for each model. The NCIT-measured temperature was compared with the oral temperature obtained using a reference oral thermometer. The mean difference between the reference thermometer and NCIT measurement (clinical bias) was different for each NCIT model. The clinical bias ranged from just under - 0.9 °C (under-reporting) to just over 0.2 °C (over-reporting). The individual differences ranged from - 3 to + 2 °C in extreme cases, with the majority of the differences between - 2 and + 1 °C. Depending upon the NCIT model, 48% to 88% of the individual temperature measurements were outside the labeled accuracy stated by the manufacturers. The sensitivity of the NCIT models for detecting subject's temperature above 38 °C ranged from 0 to 0.69. Overall, our results indicate that some NCIT devices may not be consistently accurate enough to determine if subject's temperature exceeds a specific threshold of 38 °C. Model-to-model variability and individual model accuracy in the displayed temperature were found to be outside of acceptable limits. Accuracy and credibility of the NCITs should be thoroughly evaluated before using them as an effective screening tool.

Using sufficient direction factor model to analyze latent activities associated with breast cancer survival.

High-dimensional gene expression data often exhibit intricate correlation patterns as the result of coordinated genetic regulation. In practice, however, it is difficult to directly measure these coordinated underlying activities. Analysis of breast cancer survival data with gene expressions motivates us to use a two-stage latent factor approach to estimate these unobserved coordinated biological processes. Compared to existing approaches, our proposed procedure has several unique characteristics. In the first stage, an important distinction is that our procedure incorporates prior biological knowledge about gene-pathway membership into the analysis and explicitly model the effects of genetic pathways on the latent factors. Second, to characterize the molecular heterogeneity of breast cancer, our approach provides estimates specific to each cancer subtype. Finally, our proposed framework incorporates sparsity condition due to the fact that genetic networks are often sparse. In the second stage, we investigate the relationship between latent factor activity levels and survival time with censoring using a general dimension reduction model in the survival analysis context. Combining the factor model and sufficient direction model provides an efficient way of analyzing high-dimensional data and reveals some interesting relations in the breast cancer gene expression data.

Coarse-graining of protein structures for the normal mode studies.

The coarse-grained structural model such as Gaussian network has played a vital role in the normal mode studies for understanding protein dynamics related to biological functions. However, for the large proteins, the Gaussian network model is computationally unfavorable for diagonalization of Hessian (stiffness) matrix for the normal mode studies. In this article, we provide the coarse-graining method, referred to as "dynamic model condensation," which enables the further coarse-graining of protein structures consisting of small number of residues. It is shown that the coarser-grained structures reconstructed by dynamic model condensation exhibit the dynamic characteristics, such as low-frequency normal modes, qualitatively comparable to original structures. This sheds light on that dynamic model condensation and may enable one to study the large protein dynamics for gaining insight into biological functions of proteins.