**3. Discussion**

Liposomes in themselves are relatively unstable delivery systems because of their membrane instability in aqueous solution, which can affect their bioavailability and pharmacological effect, the addition of adjuvants being necessary to bring more rigidity and stability. AST seems to enhance the liposomes' stability when on the membrane, which helps its delivery. The strategy of adding other adjuvants to improve membrane rigidity seems to be necessary to ensure stability, which should be taken into account when assessing production costs. Reducing the particle size with the development of nanoliposomes seems to be an excellent alternative method to improve their stability and consequently their penetration, solubility, and continuous release; however, it is still necessary to consider the machinery and costs involved. Another innovative alternative method to deliver AST on deep skin layers is the use of the iontophoretic technique with charged delivery systems, developed with liposomes, which makes it possible to reach the stratum corneum and act as a whitening agent.

Emulsion delivery systems, especially nanoemulsions, are an effective and widely applied form to deliver bioactive compounds in medicines and cosmetics. Due to the hydrophobic character of AST, most of the nanoemulsions prepared in the literature are

oil/water systems. Studies based on experimental design allow researchers to identify the best conditions for nanoemulsion preparation, with reduced time and costs of research. In addition, nanoemulsions can be incorporated in other delivery systems, such as films, which can significantly improve their biological effects and their acceptance by the patient or customer. Unfortunately, only one article was found concerning the use of microemulsions in dermatological and cosmetics applications. More investment in research on this new product delivery system is desirable.

Particulate systems are a promising alternative way to develop innovative formulations for AST delivery, thanks to the possibility of using different matrix components and methods to form spheres and capsules at micro- or nanoscale. Similar to nanoemulsions, studies based on experimental design can be performed to optimize the conditions needed to prepare particulate systems. It is important to consider alternative methods, to prevent AST degradation. Some articles on particulate systems considered in this review presented either ecological or natural alternatives to develop delivery systems, which can aggregate to more sustainable products.

The use of CDs as systems to guest AST in inclusion complexes is able to significantly improve the solubility of hydrophobic compounds like AST. However, it would be necessary to have a greater number of inclusion complexes, in order to carry out a comparison by which to identify the most advantageous system. From the results examined in this review, it is possible to state that AST can form inclusion complexes either with natural CDs, such as β-CDs, or with modified CDs, such as hydroxypropyl β-CD. Films for the delivery of bioactive compounds on the skin are an alternative, especially for wound healing and scald treatments. The antioxidant effect accelerates the healing process, due to its antiinflammatory activity. The AST-incorporating films may also be applied as an easy adjunct treatment, to treat skin cancer, which should be considered in the research. In addition, AST seems to act merely as an active principle, which requires a film-forming agent such as collagen, chitosan, cellulose derivatives, and others to create the film structure.

The systems that seem to be more developed and robust are the emulsions and the particulate systems, given the large number of articles and assays performed on them. However, they are not the only systems to consider. On the other hand, the limited studies carried out on other systems, many of which are not mentioned in this review article, show opportunities for the development of new and innovative formulations. Examples are solid dispersions, transdermal systems, and others.


*2.4. Data Processing*

The PCC samples were divided into two sets by systematic sampling method; 86 of them were used as calibration sets to establish the prediction models, and 20 samples not involved in the calibration were used as validation sets for external validation of the effectiveness of the developed models. The chemical determination results of anthocyanidin content obtained by HPLC of the calibration set samples were imported into the chemometric software accompanying the instrument and processed for NIR spectroscopy to obtain a cal. file. The spectral data were preprocessed using a partial least squares regression (PLSR) method at three different wavelength bands. These three bands included 400–1100 nm, 1100–2498 nm (full band); 400–800 nm (visible band); 800–1100 nm, 1100–2498 nm (near infrared band). The pre-processing scattering model of the spectral data included no scattering processing (None), standard normal variables transformation + de-trending processing (SNV+Detrend), standard normal variation processing (SNV Only), de-trending processing (Detrend Only), standard multivariate scattering correction (Standard MSC), weighted multivariate scattering correction (Weighted MSC), and two different derivative treatments, namely, no derivative and first-order derivative were employed. The final prediction models built under different preprocessing methods were compared, and the model with the internal cross-validation correlation coefficient (1-VR) close to 1 and lower standard error of cross-validation (SECV) was selected as the best one. These two sets of data can basically reflect the prediction performance of the calibration model for unknown samples. Subsequently, samples of the

validation set were analyzed to test the predictive ability of the proposed model. The criterion was that the higher the external correlation coefficient (RSQ) value and the lower the standard deviation of prediction (SEP), the more accurate the model.


*Edited by Felicia M. Ferreri*

Dry eye syndrome is one of the most common types of ocular surface disorders that significantly worsen the quality of life of tens of millions of people worldwide.In the last decades, researchers worldwide investigated the composition and regulatory mechanisms of the preocular tear film to better understand dry eye syndrome. The tear film, in fact, plays a critical role in maintaining corneal and conjunctival integrity, protecting the eyes against infections, and preserving visual acuity. Recent scientific discoveries helped us gain a more and more accurate understanding of the structure and functioning of the tear film and how disorders in the tear film relate to dry eye syndrome. Today, ophthalmologists benefit from sophisticated diagnostic techniques, and they have at their disposal a wide range of effective therapeutic options to manage dry eye syndrome. This book illustrates the most recent research results in the diagnosis and treatment of dry eye syndrome, and it is of interest to the broad audience that comprises ophthalmologists, researchers, and students.

Published in London, UK © 2022 IntechOpen © herraez / iStock

Dry Eye Syndrome - Modern Diagnostic Techniques and Advanced Treatments

Dry Eye Syndrome

Modern Diagnostic Techniques

and Advanced Treatments

*Edited by Felicia M. Ferreri*


**4. Experiment**

### **4.1 Dataset preparation**

In the dataset, the total number of images of rejuvenation treatment and eye double-fold surgery are 5585 and 36,598 pairs of the images, respectively. The data is filtered to select a good image for training. Some errors in the images are not well aligned, only one eye, etc. The reject rates are 82.15 and 59.92%, respectively.

The image was crop around the eye area. Then the data is divided into 70, 15, and 15% for training, validation, and testing, respectively.

As mentioned earlier, the output of the model is the probability of perfection. Hence, our training data include the image of before and after surgery as shown in **Figure 3**.

#### **4.2 Prediction accuracy**

We chose a thresholding value for the prediction model. If the predicted value is higher than the thresholding value, the face is predicted to be perfect. From that, we obtain accuracy of the different models as shown in **Tables 2** and **3**. We have two schemes of training in which the feature extractors are freezed (not training together with the classification model) and trainable, resulting in eight models in these two tables. When the feature extractor is freezed, we believe that it has already captured universal features such as edge and curves which is relevant to our tasks. Therefore, we want to keep the weights of the feature extract intact. However, in the second training scheme, we apply different learning rates for the feature extractor and the fully connected layers. The learning rate of the feature extractor is much smaller than that of the fully connected layer because we believe that the weights of the feature extractor is good enough for our tasks and we do not want to distort them too quickly and too much during the training of the classification model.

These above training schemes are the best common practices when finetuning deep neural networks. We tried both of them, resulting in the following. The best model for double-fold surgery and rejuvenation treatment are Models 1 and 8 (see **Tables 2** and **3** for more details), with accuracy on the test dataset of 93.1 and 88.9%, respectively. Model 1 is the model when the encoder

**49**

than 1%.

**Table 3.**

**Table 2.**

**Figure 3.**

*Testing accuracy for eye double-fold surgery.*

*Testing accuracy for rejuvenation treatment.*

**5. Conclusion**

*A Deep Learning-Based Aesthetic Surgery Recommendation System*

was freezed. However, in Model 8, the encoder was retrained. However, the accuracy differences between the best model and the second best model are less

We have presented in this chapter an interesting application of deep learning in aesthetic surgery recommendation along with its encouraging results. By using our system, the presented deep learning engine will provide a reference decision of taking either rejuvenation treatment or eye double-fold surgery or not to both the surgeon and the patient, just based on the eye photo of the patient. To this end, we trained a deep autoencoder by our in-house dataset, composing of pairs of images captured before and after the surgery. The trained encoder part learned in an unsupervised manner, a rich set of features, characterized both unattractive and beautiful facial features. We concatenate the trained encoder part to a fully connected layer to predict perfection score of an eye photo of a patient, based on which

a decision of taking treatment or not will be made.

*DOI: http://dx.doi.org/10.5772/intechopen.86411*

*Example of original and outcome of surgery.*

*A Deep Learning-Based Aesthetic Surgery Recommendation System DOI: http://dx.doi.org/10.5772/intechopen.86411*

#### **Figure 3.**

*Advanced Analytics and Artificial Intelligence Applications*

**48**

**4. Experiment**

**Table 1.**

**Figure 3**.

**4.1 Dataset preparation**

*(2.2) upsampling with transposed convolution.*

*Autoencoder model and classification model.*

**4.2 Prediction accuracy**

of the classification model.

In the dataset, the total number of images of rejuvenation treatment and eye double-fold surgery are 5585 and 36,598 pairs of the images, respectively. The data is filtered to select a good image for training. Some errors in the images are not well aligned, only one eye, etc. The reject rates are 82.15 and 59.92%, respectively. The image was crop around the eye area. Then the data is divided into 70, 15,

*conv: convolution. conv\*: convolution and max-pooling with filter size (2.2). deconv: transposed convolution. deconv\*:*

As mentioned earlier, the output of the model is the probability of perfection. Hence, our training data include the image of before and after surgery as shown in

We chose a thresholding value for the prediction model. If the predicted value is higher than the thresholding value, the face is predicted to be perfect. From that, we obtain accuracy of the different models as shown in **Tables 2** and **3**. We have two schemes of training in which the feature extractors are freezed (not training together with the classification model) and trainable, resulting in eight models in these two tables. When the feature extractor is freezed, we believe that it has already captured universal features such as edge and curves which is relevant to our tasks. Therefore, we want to keep the weights of the feature extract intact. However, in the second training scheme, we apply different learning rates for the feature extractor and the fully connected layers. The learning rate of the feature extractor is much smaller than that of the fully connected layer because we believe that the weights of the feature extractor is good enough for our tasks and we do not want to distort them too quickly and too much during the training

These above training schemes are the best common practices when finetuning deep neural networks. We tried both of them, resulting in the following. The best model for double-fold surgery and rejuvenation treatment are Models 1 and 8 (see **Tables 2** and **3** for more details), with accuracy on the test dataset of 93.1 and 88.9%, respectively. Model 1 is the model when the encoder

and 15% for training, validation, and testing, respectively.

*Example of original and outcome of surgery.*

#### **Table 2.**

*Testing accuracy for eye double-fold surgery.*

#### **Table 3.**

*Testing accuracy for rejuvenation treatment.*

was freezed. However, in Model 8, the encoder was retrained. However, the accuracy differences between the best model and the second best model are less than 1%.