Outcome prediction in endoscopic surgery for chronic rhinosinusitis - a multidimensional model

The Evolution and Application of Artificial Intelligence in Rhinology: A State of the Art Review

OBJECTIVE

To provide a comprehensive overview on the applications of artificial intelligence (AI) in rhinology, highlight its limitations, and propose strategies for its integration into surgical practice.

DATA SOURCES

Medline, Embase, CENTRAL, Ei Compendex, IEEE, and Web of Science.

REVIEW METHODS

English studies from inception until January 2022 and those focusing on any application of AI in rhinology were included. Study selection was independently performed by 2 authors; discrepancies were resolved by the senior author. Studies were categorized by rhinology theme, and data collection comprised type of AI utilized, sample size, and outcomes, including accuracy and precision among others.

CONCLUSIONS

An overall 5435 articles were identified. Following abstract and title screening, 130 articles underwent full-text review, and 59 articles were selected for analysis. Eleven studies were from the gray literature. Articles were stratified into image processing, segmentation, and diagnostics (n = 27); rhinosinusitis classification (n = 14); treatment and disease outcome prediction (n = 8); optimizing surgical navigation and phase assessment (n = 3); robotic surgery (n = 2); olfactory dysfunction (n = 2); and diagnosis of allergic rhinitis (n = 3). Most AI studies were published from 2016 onward (n = 45).

IMPLICATIONS FOR PRACTICE

This state of the art review aimed to highlight the increasing applications of AI in rhinology. Next steps will entail multidisciplinary collaboration to ensure data integrity, ongoing validation of AI algorithms, and integration into clinical practice. Future research should be tailored at the interplay of AI with robotics and surgical education.

A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI

Skin cancer is a serious disease that affects people all over the world. Melanoma is an aggressive form of skin cancer, and early detection can significantly reduce human mortality. In the United States, approximately 97,610 new cases of melanoma will be diagnosed in 2023. However, challenges such as lesion irregularities, low-contrast lesions, intraclass color similarity, redundant features, and imbalanced datasets make improved recognition accuracy using computerized techniques extremely difficult. This work presented a new framework for skin lesion recognition using data augmentation, deep learning, and explainable artificial intelligence. In the proposed framework, data augmentation is performed at the initial step to increase the dataset size, and then two pretrained deep learning models are employed. Both models have been fine-tuned and trained using deep transfer learning. Both models (Xception and ShuffleNet) utilize the global average pooling layer for deep feature extraction. The analysis of this step shows that some important information is missing; therefore, we performed the fusion. After the fusion process, the computational time was increased; therefore, we developed an improved Butterfly Optimization Algorithm. Using this algorithm, only the best features are selected and classified using machine learning classifiers. In addition, a GradCAM-based visualization is performed to analyze the important region in the image. Two publicly available datasets-ISIC2018 and HAM10000-have been utilized and obtained improved accuracy of 99.3% and 91.5%, respectively. Comparing the proposed framework accuracy with state-of-the-art methods reveals improved and less computational time.

Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach

COVID-19 is a disease caused by the SARS-CoV-2 virus. The COVID-19 virus spreads when a person comes into contact with an affected individual. This is mainly through drops of saliva or nasal discharge. Most of the affected people have mild symptoms while some people develop acute respiratory distress syndrome (ARDS), which damages organs like the lungs and heart. Chest X-rays (CXRs) have been widely used to identify abnormalities that help in detecting the COVID-19 virus. They have also been used as an initial screening procedure for individuals highly suspected of being infected. However, the availability of radiographic CXRs is still scarce. This can limit the performance of deep learning (DL) based approaches for COVID-19 detection. To overcome these limitations, in this work, we developed an Auxiliary Classifier Generative Adversarial Network (ACGAN), to generate CXRs. Each generated X-ray belongs to one of the two classes COVID-19 positive or normal. To ensure the goodness of the synthetic images, we performed some experimentation on the obtained images using the latest Convolutional Neural Networks (CNNs) to detect COVID-19 in the CXRs. We fine-tuned the models and achieved more than 98% accuracy. After that, we also performed feature selection using the Harmony Search (HS) algorithm, which reduces the number of features while retaining classification accuracy. We further release a GAN-generated dataset consisting of 500 COVID-19 radiographic images.

Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization

Manual diagnosis of skin cancer is time-consuming and expensive; therefore, it is essential to develop automated diagnostics methods with the ability to classify multiclass skin lesions with greater accuracy. We propose a fully automated approach for multiclass skin lesion segmentation and classification by using the most discriminant deep features. First, the input images are initially enhanced using local color-controlled histogram intensity values (LCcHIV). Next, saliency is estimated using a novel Deep Saliency Segmentation method, which uses a custom convolutional neural network (CNN) of ten layers. The generated heat map is converted into a binary image using a thresholding function. Next, the segmented color lesion images are used for feature extraction by a deep pre-trained CNN model. To avoid the curse of dimensionality, we implement an improved moth flame optimization (IMFO) algorithm to select the most discriminant features. The resultant features are fused using a multiset maximum correlation analysis (MMCA) and classified using the Kernel Extreme Learning Machine (KELM) classifier. The segmentation performance of the proposed methodology is analyzed on ISBI 2016, ISBI 2017, ISIC 2018, and PH2 datasets, achieving an accuracy of 95.38%, 95.79%, 92.69%, and 98.70%, respectively. The classification performance is evaluated on the HAM10000 dataset and achieved an accuracy of 90.67%. To prove the effectiveness of the proposed methods, we present a comparison with the state-of-the-art techniques.

Effect of diabetes mellitus on postoperative endoscopic sinus surgery outcomes

BACKGROUND

Endoscopic sinus surgery (ESS) has become the treatment of choice for a variety of nasal conditions. The purpose of this study was to analyze the effect of diabetes mellitus (DM) on postoperative outcomes in ESS.

METHODS

Data on endoscopic sinus surgery performed from 2005 to 2013 were collected from the American College of Surgeons National Surgical Quality Improvement (ACS-NSQIP) database. Two groups were created, based on the presence of a DM diagnosis, and were analyzed for preoperative variables, comorbidities, and postoperative complications using SPSS statistical software.

RESULTS

There were 644 patients included in the analysis, 85 of whom (13.2%) had a diagnosis of DM. Patients with DM were more likely to have higher rates of preoperative dyspnea and hypertension. After accounting for confounding factors, DM patients undergoing ESS were at higher risk of overall medical complications, pneumonia, unplanned reintubation, ventilator use of >48 hours, and mortality. However, after separating patients into outpatient and inpatient groups, DM was found to be an independent predictor of urinary tract infection in outpatients and of ventilator use >48 hours in inpatients.

CONCLUSION

DM patients undergoing ESS are at increased risk for postoperative medical complications. However, DM does not appear to increase the postoperative surgical complication rate in this population. Furthermore, DM does not appear to have an impact on ESS mortality, readmission, or reoperation rates.

Asymmetric reduction of ketones and β-keto esters by (S)-1-phenylethanol dehydrogenase from denitrifying bacterium Aromatoleum aromaticum

Enzyme-catalyzed enantioselective reductions of ketones and keto esters have become popular for the production of homochiral building blocks which are valuable synthons for the preparation of biologically active compounds at industrial scale. Among many kinds of biocatalysts, dehydrogenases/reductases from various microorganisms have been used to prepare optically pure enantiomers from carbonyl compounds. (S)-1-phenylethanol dehydrogenase (PEDH) was found in the denitrifying bacterium Aromatoleum aromaticum (strain EbN1) and belongs to the short-chain dehydrogenase/reductase family. It catalyzes the stereospecific oxidation of (S)-1-phenylethanol to acetophenone during anaerobic ethylbenzene mineralization, but also the reverse reaction, i.e., NADH-dependent enantioselective reduction of acetophenone to (S)-1-phenylethanol. In this work, we present the application of PEDH for asymmetric reduction of 42 prochiral ketones and 11 β-keto esters to enantiopure secondary alcohols. The high enantioselectivity of the reaction is explained by docking experiments and analysis of the interaction and binding energies of the theoretical enzyme-substrate complexes leading to the respective (S)- or (R)-alcohols. The conversions were carried out in a batch reactor using Escherichia coli cells with heterologously produced PEDH as whole-cell catalysts and isopropanol as reaction solvent and cosubstrate for NADH recovery. Ketones were converted to the respective secondary alcohols with excellent enantiomeric excesses and high productivities. Moreover, the progress of product formation was studied for nine para-substituted acetophenone derivatives and described by neural network models, which allow to predict reactor behavior and provides insight on enzyme reactivity. Finally, equilibrium constants for conversion of these substrates were derived from the progress curves of the reactions. The obtained values matched very well with theoretical predictions.