Well-Being in Plastic Surgery: Deep Learning Reveals Patients’ Evaluations

2021 ◽  
Author(s):  
Joschka Kersting ◽  
Michaela Geierhos
Keyword(s):  
Deep Learning ◽  
Plastic Surgery ◽  
Well Being

Plastic Surgery: Review on Screening of Psychiatric Disorder

2021 ◽  
Vol 1 (5) ◽  
pp. 91-94
Author(s):  
Angeline Fenisenda
Keyword(s):  
Psychiatric Disorder ◽  
Plastic Surgery ◽  
Cosmetic Surgery ◽  
Psychiatric Patients ◽  
Emotional Adjustment ◽  
Aesthetic Surgery ◽  
Well Being ◽  
Pilot Studies ◽  
Cosmetic Procedure ◽  
Poor Outcomes

Recently the popularity of plastic surgical procedure has been increased. Many people surgically alter their physical appearance with the intent of boosting their social and psychological well-being; however, the long-term effectiveness of aesthetic surgery on improving well-being is unconfirmed. To have successful cosmetic plastic surgery result, it is imperative to assess candidates for predictors of poor outcomes. These include the following factor : psychiatric disorder, demographic factors (male and younger age), relationship issues, unrealistic expectations, previous dissatisfied surgery, and minimal deformity. For psychiatric patients, despite having technically satisfactory cosmetic surgery, poor emotional adjustment and social functioning were seen post procedure. Proper screening and evaluation of these patients could save money and resources. In this brief review we discuss about psychiatric disorder screen on plastic surgery to prevent unwanted outcome. A literature review was conducted in the electronic database PubMed using keyword “Psychiatric Disorder”, “Plastic surgery”, “Prevention” and “Screening”. All type of studies were included for this study, such as controlled trials, systematic reviews, literature reviews, and pilot studies published between 2011 and 2021. Articles which not written in English were excluded from the study. This search resulted in 18 papers. Some patient who undergo cosmetic surgery suffer from underdiagnosed or untreated psychiatric disorder. To avoid unnecessary expense and resource it is advisable that all patient who seek cosmetic procedure undergo psychiatric screen such as PHQ-9, GAD-7, BDDQ and other test to avoid un-necessary expense and resource.

scholarly journals Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

2021 ◽  
Author(s):  
Pranjal Kumar
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Well Being ◽  
Human Activity Recognition ◽  
Research Field ◽  
Daily Lives ◽  
Video Cameras ◽  
Advantages And Disadvantages

Human Activity Recognition (HAR) has become a vibrant research field over the last decade, especially because of the spread of electronic devices like mobile phones, smart cell phones, and video cameras in our daily lives. In addition, the progress of deep learning and other algorithms has made it possible for researchers to use HAR in many fields including sports, health, and well-being. HAR is, for example, one of the most promising resources for helping older people with the support of their cognitive and physical function through day-to-day activities. This study focuses on the key role machine learning plays in the development of HAR applications. While numerous HAR surveys and review articles have previously been carried out, the main/overall HAR issue was not taken into account, and these studies concentrate only on specific HAR topics. A detailed review paper covering major HAR topics is therefore essential. This study analyses the most up-to-date studies on HAR in recent years and provides a classification of HAR methodology and demonstrates advantages and disadvantages for each group of methods. This paper finally addresses many problems in the current HAR subject and provides recommendations for potential study.

scholarly journals Automated Video Behavior Recognition of Pigs Using Two-Stream Convolutional Networks

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1085
Author(s):  
Kaifeng Zhang ◽  
Dan Li ◽  
Jiayun Huang ◽  
Yifei Chen
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Optical Flow ◽  
Network Models ◽  
Well Being ◽  
Motion Information ◽  
Behavior Recognition ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Effective Manner

The detection of pig behavior helps detect abnormal conditions such as diseases and dangerous movements in a timely and effective manner, which plays an important role in ensuring the health and well-being of pigs. Monitoring pig behavior by staff is time consuming, subjective, and impractical. Therefore, there is an urgent need to implement methods for identifying pig behavior automatically. In recent years, deep learning has been gradually applied to the study of pig behavior recognition. Existing studies judge the behavior of the pig only based on the posture of the pig in a still image frame, without considering the motion information of the behavior. However, optical flow can well reflect the motion information. Thus, this study took image frames and optical flow from videos as two-stream input objects to fully extract the temporal and spatial behavioral characteristics. Two-stream convolutional network models based on deep learning were proposed, including inflated 3D convnet (I3D) and temporal segment networks (TSN) whose feature extraction network is Residual Network (ResNet) or the Inception architecture (e.g., Inception with Batch Normalization (BN-Inception), InceptionV3, InceptionV4, or InceptionResNetV2) to achieve pig behavior recognition. A standard pig video behavior dataset that included 1000 videos of feeding, lying, walking, scratching and mounting from five kinds of different behavioral actions of pigs under natural conditions was created. The dataset was used to train and test the proposed models, and a series of comparative experiments were conducted. The experimental results showed that the TSN model whose feature extraction network was ResNet101 was able to recognize pig feeding, lying, walking, scratching, and mounting behaviors with a higher average of 98.99%, and the average recognition time of each video was 0.3163 s. The TSN model (ResNet101) is superior to the other models in solving the task of pig behavior recognition.

Applied Deep Learning in Plastic Surgery

2020 ◽  
Vol 31 (1) ◽  
pp. 102-106 ◽  
Author(s):  
Emily Borsting ◽  
Robert DeSimone ◽  
Mustafa Ascha ◽  
Mona Ascha
Keyword(s):  
Deep Learning ◽  
Plastic Surgery

scholarly journals Detection of Preventable Fetal Distress During Labor From Scanned Cardiotocogram Tracings Using Deep Learning

2021 ◽  
Vol 9 ◽  
Author(s):  
Martin G. Frasch ◽  
Shadrian B. Strong ◽  
David Nilosek ◽  
Joshua Leaverton ◽  
Barry S. Schifrin
Keyword(s):  
Deep Learning ◽  
Fetal Monitoring ◽  
Fetal Distress ◽  
Well Being ◽  
Adverse Outcomes ◽  
Care Providers ◽  
Learning Framework ◽  
Uterine Contractions ◽  
Neuroprotective Treatment ◽  
Pattern Information

Despite broad application during labor and delivery, there remains considerable debate about the value of electronic fetal monitoring (EFM). EFM includes the surveillance of fetal heart rate (FHR) patterns in conjunction with the mother's uterine contractions, providing a wealth of data about fetal behavior and the threat of diminished oxygenation and cerebral perfusion. Adverse outcomes universally associate a fetal injury with the failure to timely respond to FHR pattern information. Historically, the EFM data, stored digitally, are available only as rasterized pdf images for contemporary or historical discussion and examination. In reality, however, they are rarely reviewed systematically or purposefully. Using a unique archive of EFM collected over 50 years of practice in conjunction with adverse outcomes, we present a deep learning framework for training and detection of incipient or past fetal injury. We report 94% accuracy in identifying early, preventable fetal injury intrapartum. This framework is suited for automating an early warning and decision support system for maintaining fetal well-being during the stresses of labor. Ultimately, such a system could enable obstetrical care providers to timely respond during labor and prevent both urgent intervention and adverse outcomes. When adverse outcomes cannot be avoided, they can provide guidance to the early neuroprotective treatment of the newborn.

scholarly journals The Impact of Cosmetic Plastic Surgery on Body Image and Psychological Well-Being: A Preliminary Study

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Valentina Mattei ◽  
Elena Bagliacca ◽  
Alessandro Ambrosi ◽  
Luciano Lanfranchi ◽  
Franz Preis ◽  
...  
Keyword(s):  
Body Image ◽  
Plastic Surgery ◽  
Well Being ◽  
Psychological Well Being ◽  
Preliminary Study ◽  
The Impact

scholarly journals Light Actuation Based On Facial Mood Recognition

2020 ◽  
Vol 9 (05) ◽  
pp. 25052-25056
Author(s):  
Abhi Kadam ◽  
Anupama Mhatre ◽  
Sayali Redasani ◽  
Amit Nerurkar
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Well Being ◽  
Training Data ◽  
Facial Emotion ◽  
Specific Data ◽  
Affective Meaning ◽  
Deep Learning Model ◽  
And Training

Current lighting technologies extend the options for changing the appearance of rooms and closed spaces, as such creating ambiences with an affective meaning. Using intelligence, these ambiences may instantly be adapted to the needs of the room’s occupant(s), possibly improving their well-being. In this paper, we set actuate lighting in our surrounding using mood detection. We analyze the mood of the person by Facial Emotion Recognition using deep learning model such as Convolutional Neural Network (CNN). On recognizing this emotion, we will actuate lighting in our surrounding in accordance with the mood. Based on implementation results, the system needs to be developed further by adding more specific data class and training data.

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