Gastric Cancer Detection with Ensemble Learning on Digital Pathology: Use Case of Gastric Cancer on GasHisSDB Dataset.

Gastric cancer has become a serious worldwide health concern, emphasizing the crucial importance of early diagnosis measures to improve patient outcomes. While traditional histological image analysis is regarded as the clinical gold standard, it is labour intensive and manual. In recognition of this problem, there has been a rise in interest in the use of computer-aided diagnostic tools to help pathologists with their diagnostic efforts. In particular, deep learning (DL) has emerged as a promising solution in this sector. However, current DL models are still restricted in their ability to extract extensive visual characteristics for correct categorization. To address this limitation, this study proposes the use of ensemble models, which incorporate the capabilities of several deep-learning architectures and use aggregate knowledge of many models to improve classification performance, allowing for more accurate and efficient gastric cancer detection. To determine how well these proposed models performed, this study compared them with other works, all of which were based on the Gastric Histopathology Sub-Size Images Database, a publicly available dataset for gastric cancer. This research demonstrates that the ensemble models achieved a high detection accuracy across all sub-databases, with an average accuracy exceeding 99%. Specifically, ResNet50, VGGNet, and ResNet34 performed better than EfficientNet and VitNet. For the 80 × 80-pixel sub-database, ResNet34 exhibited an accuracy of approximately 93%, VGGNet achieved 94%, and the ensemble model excelled with 99%. In the 120 × 120-pixel sub-database, the ensemble model showed 99% accuracy, VGGNet 97%, and ResNet50 approximately 97%. For the 160 × 160-pixel sub-database, the ensemble model again achieved 99% accuracy, VGGNet 98%, ResNet50 98%, and EfficientNet 92%, highlighting the ensemble model's superior performance across all resolutions. Overall, the ensemble model consistently provided an accuracy of 99% across the three sub-pixel categories. These findings show that ensemble models may successfully detect critical characteristics from smaller patches and achieve high performance. The findings will help pathologists diagnose gastric cancer using histopathological images, leading to earlier identification and higher patient survival rates.

Multi-Scale Digital Pathology Patch-Level Prostate Cancer Grading Using Deep Learning: Use Case Evaluation of DiagSet Dataset.

Prostate cancer remains a prevalent health concern, emphasizing the critical need for early diagnosis and precise treatment strategies to mitigate mortality rates. The accurate prediction of cancer grade is paramount for timely interventions. This paper introduces an approach to prostate cancer grading, framing it as a classification problem. Leveraging ResNet models on multi-scale patch-level digital pathology and the Diagset dataset, the proposed method demonstrates notable success, achieving an accuracy of 0.999 in identifying clinically significant prostate cancer. The study contributes to the evolving landscape of cancer diagnostics, offering a promising avenue for improved grading accuracy and, consequently, more effective treatment planning. By integrating innovative deep learning techniques with comprehensive datasets, our approach represents a step forward in the pursuit of personalized and targeted cancer care.

Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology.

Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens.

Clinical use of polygenic risk scores for detection of peripheral artery disease and cardiovascular events.

We have previously shown that polygenic risk scores (PRS) can improve risk stratification of peripheral artery disease (PAD) in a large, retrospective cohort. Here, we evaluate the potential of PRS in improving the detection of PAD and prediction of major adverse cardiovascular and cerebrovascular events (MACCE) and adverse events (AE) in an institutional patient cohort. We created a cohort of 278 patients (52 cases and 226 controls) and fit a PAD-specific PRS based on the weighted sum of risk alleles. We built traditional clinical risk models and machine learning (ML) models using clinical and genetic variables to detect PAD, MACCE, and AE. The models' performances were measured using the area under the curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), and Brier score. We also evaluated the clinical utility of our PAD model using decision curve analysis (DCA). We found a modest, but not statistically significant improvement in the PAD detection model's performance with the inclusion of PRS from 0.902 (95% CI: 0.846-0.957) (clinical variables only) to 0.909 (95% CI: 0.856-0.961) (clinical variables with PRS). The PRS inclusion significantly improved risk re-classification of PAD with an NRI of 0.07 (95% CI: 0.002-0.137), p = 0.04. For our ML model predicting MACCE, the addition of PRS did not significantly improve the AUC, however, NRI analysis demonstrated significant improvement in risk re-classification (p = 2e-05). Decision curve analysis showed higher net benefit of our combined PRS-clinical model across all thresholds of PAD detection. Including PRS to a clinical PAD-risk model was associated with improvement in risk stratification and clinical utility, although we did not see a significant change in AUC. This result underscores the potential clinical utility of incorporating PRS data into clinical risk models for prevalent PAD and the need for use of evaluation metrics that can discern the clinical impact of using new biomarkers in smaller populations.

Enhancing Prostate Cancer Diagnosis with a Novel Artificial Intelligence-Based Web Application: Synergizing Deep Learning Models, Multimodal Data, and Insights from Usability Study with Pathologists.

Prostate cancer remains a significant cause of male cancer mortality in the United States, with an estimated 288,300 new cases in 2023. Accurate grading of prostate cancer is crucial for ascertaining disease severity and shaping treatment strategies. Modern deep learning techniques show promise in grading biopsies, but there is a gap in integrating these advances into clinical practice. Our web platform tackles this challenge by integrating human expertise with AI-driven grading, incorporating diverse data sources. We gathered feedback from four pathologists and one medical practitioner to assess usability and real-world alignment through a survey and the NASA TLX Usability Test. Notably, 60% of users found it easy to navigate, rating it 5.5 out of 7 for ease of understanding. Users appreciated self-explanatory information in popup tabs. For ease of use, all users favored the detailed summary tab, rating it 6.5 out of 7. While 80% felt patient demographics beyond age were unnecessary, high-resolution biopsy images were deemed vital. Acceptability was high, with all users willing to adopt the app, and some believed it could reduce workload. The NASA TLX Usability Test indicated a low-moderate perceived workload, suggesting room for improved explanations and data visualization.

Physician- and Patient-Elicited Barriers and Facilitators to Implementation of a Machine Learning-Based Screening Tool for Peripheral Arterial Disease: Preimplementation Study With Physician and Patient Stakeholders.

BACKGROUND: Peripheral arterial disease (PAD) is underdiagnosed, partially due to a high prevalence of atypical symptoms and a lack of physician and patient awareness. Implementing clinical decision support tools powered by machine learning algorithms may help physicians identify high-risk patients for diagnostic workup. OBJECTIVE: This study aims to evaluate barriers and facilitators to the implementation of a novel machine learning-based screening tool for PAD among physician and patient stakeholders using the Consolidated Framework for Implementation Research (CFIR). METHODS: We performed semistructured interviews with physicians and patients from the Stanford University Department of Primary Care and Population Health, Division of Cardiology, and Division of Vascular Medicine. Participants answered questions regarding their perceptions toward machine learning and clinical decision support for PAD detection. Rapid thematic analysis was performed using templates incorporating codes from CFIR constructs. RESULTS: A total of 12 physicians (6 primary care physicians and 6 cardiovascular specialists) and 14 patients were interviewed. Barriers to implementation arose from 6 CFIR constructs: complexity, evidence strength and quality, relative priority, external policies and incentives, knowledge and beliefs about intervention, and individual identification with the organization. Facilitators arose from 5 CFIR constructs: intervention source, relative advantage, learning climate, patient needs and resources, and knowledge and beliefs about intervention. Physicians felt that a machine learning-powered diagnostic tool for PAD would improve patient care but cited limited time and authority in asking patients to undergo additional screening procedures. Patients were interested in having their physicians use this tool but raised concerns about such technologies replacing human decision-making. CONCLUSIONS: Patient- and physician-reported barriers toward the implementation of a machine learning-powered PAD diagnostic tool followed four interdependent themes: (1) low familiarity or urgency in detecting PAD; (2) concerns regarding the reliability of machine learning; (3) differential perceptions of responsibility for PAD care among primary care versus specialty physicians; and (4) patient preference for physicians to remain primary interpreters of health care data. Facilitators followed two interdependent themes: (1) enthusiasm for clinical use of the predictive model and (2) willingness to incorporate machine learning into clinical care. Implementation of machine learning-powered diagnostic tools for PAD should leverage provider support while simultaneously educating stakeholders on the importance of early PAD diagnosis. High predictive validity is necessary for machine learning models but not sufficient for implementation.

Global Advancement in Pharmacy Services for Mental Health: A Review for Evidence-Based Practices.

The symptoms of psychiatric infirmities have variability, and selected drug regimens for mental illness are comparatively complex and individualized; therefore, pharmacy services vary with respect to patients, diseases, healthcare settings, community structures, and countries. Clinical pharmacy services for mental health (MH) are continuously being upgraded. A structured search of the literature was performed in the Cochrane, PubMed (Medline), PsycINFO, Google scholar, Scopus, Science Direct, and Springer Links databases. The title and abstract of each retrieved article were evaluated for relevance. To remove uncertainty and ambiguity, the full-text articles were retrieved and examined for relevance. The articles were further assessed on the basis of inclusion and exclusion criteria. Narrative synthesis was performed, creating new categories and relevant subcategories and further subsections. The articles and the results were assessed for quality and bias. Pharmacists have a range of expertise in psychiatric care. The services can be classified as conventional, extended, and advanced pharmacy services. Conventional services include the quality use of medicines in healthcare settings and medication support services in communities that ensure medication adherence. Pharmacists perform extended roles in collaborative medication therapy management, multidisciplinary community mental health teams, collaborative care, patient education, home medication review, hospital-to-home transit, and screening services. In the USA, the role of pharmacists was advanced by prescribing as collaborative and interim prescribers. Australia launched an accredited program for psychiatric first-aid pharmacists. Pharmacists can provide mental care to rural populations using health technology. The role of pharmacists in MH is appreciated either independently or as a team member. Patients and healthcare providers rank the services of pharmacists in MH highly. Still, there is a margin for improvement in the training of pharmacists. Pharmacists cannot provide sufficient time to their patients. Public awareness about the role of pharmacists in MH needs more attention. Moreover, the training of psychiatric pharmacists should be standardized around the world.

Influence of solvent selection and extraction methods on the determination of polyphenols, antioxidant, lipoxygenase and tyrosinase inhibition activities of Opuntia ficus-indica fruits peel and pulp collected from the Kingdom of Saudi Arabia (KSA).

The effect of extracting solvents used by two methods on the TPC, TFC, antioxidant as well as lipoxygenase, and tyrosinase inhibition activities of O. ficus-indica fruit (peel and pulp) were studied. The results manifest that extracts with solvent polarities showed different levels of polyphenols contents and antioxidant activities. The extracts acquired by the Soxhlet method were the most fascinating. Interestingly, peel extracts contain more polyphenols than pulp and showed activities. Lipoxygenase and tyrosinase inhibitory activity of the fruit peel and pulp extracts was reported for the first time. The promising results obtained prompted to the formulation of a stable phytocosmetic emulsion system loaded with 1% pre-concentrated peel extract, aiming to revive facial skin properties. The efficacy of the formulations was determined through SPF and UVA protection factors. To the in vitro safety assessment CAM-TBS, HET-CAM, and red blood cell tests were achieved. Importantly, the formulation did not induce any toxicity.

Applications of AI in multi-modal imaging for cardiovascular disease.

Data for healthcare is diverse and includes many different modalities. Traditional approaches to Artificial Intelligence for cardiovascular disease were typically limited to single modalities. With the proliferation of diverse datasets and new methods in AI, we are now able to integrate different modalities, such as magnetic resonance scans, computerized tomography scans, echocardiography, x-rays, and electronic health records. In this paper, we review research from the last 5 years in applications of AI to multi-modal imaging. There have been many promising results in registration, segmentation, and fusion of different magnetic resonance imaging modalities with each other and computer tomography scans, but there are still many challenges that need to be addressed. Only a few papers have addressed modalities such as x-ray, echocardiography, or non-imaging modalities. As for prediction or classification tasks, there have only been a couple of papers that use multiple modalities in the cardiovascular domain. Furthermore, no models have been implemented or tested in real world cardiovascular clinical settings.

Validity of serum and urinary hepcidin as biomarkers for late-onset sepsis in premature infants.

Background: Sepsis remains one of the leading causes of neonatal morbidity and mortality, particularly among premature infants. Blood culture is the 'gold standard' for the diagnosis of neonatal sepsis but is associated with several pitfalls. Aim of the work: We aim to evaluate the validity of measuring serum (S.Hep) and urinary hepcidin (U.Hep) concentrations as diagnostic biomarkers for late-onset sepsis (LOS) in preterm infants. Patients and Methods: The current case-control study included 73 cases of clinically and laboratory confirmed late-onset sepsis as the 'case group' and 50 nonseptic premature infants of comparable age and sex as the 'control group'. S.Hep and U.Hep concentrations were evaluated at enrollment (acute sample) and after 1 week of treatment (convalescent sample). Results: Patients had a statistically significant higher concentration of both S.Hep and U.Hep as compared with nonseptic controls (p = 0.0001). Similarly, a significant reduction of both S.Hep and U.Hep was detected after 1 week of treatment (p = 0.001). A cut-off value of ⩾ 94.8 ng/ml of S.Hep and ⩾ 264 ng/mg of U.Hep/urinary creatinine showed high sensitivity, specificity, and positive predictive value in the diagnosis of neonatal LOS. Conclusions: Both S.Hep and U.Hep can function as promising accurate and rapid surrogate tests for the diagnosis of LOS. U.Hep measurement has the advantage of being noninvasive, with no hazards of phlebotomy, and is less variable throughout the day.

Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care.

Today's digital health revolution aims to improve the efficiency of healthcare delivery and make care more personalized and timely. Sources of data for digital health tools include multiple modalities such as electronic medical records (EMR), radiology images, and genetic repositories, to name a few. While historically, these data were utilized in silos, new machine learning (ML) and deep learning (DL) technologies enable the integration of these data sources to produce multi-modal insights. Data fusion, which integrates data from multiple modalities using ML and DL techniques, has been of growing interest in its application to medicine. In this paper, we review the state-of-the-art research that focuses on how the latest techniques in data fusion are providing scientific and clinical insights specific to the field of cardiovascular medicine. With these new data fusion capabilities, clinicians and researchers alike will advance the diagnosis and treatment of cardiovascular diseases (CVD) to deliver more timely, accurate, and precise patient care.

Postoperative Intracardiac Thrombus in a Child with Nephrotic Syndrome.

Background: Thromboembolic events are a known complication of nephrotic syndrome in children. Intracardiac thrombi are a rare location for thrombus formation but can lead to severe clinical consequences. Case Presentation: We report an intracardiac left atrial thrombus in a child with new onset steroid-resistant nephrotic syndrome and recently repaired atrial septal defect and cor triatriatum. The thrombus was successfully treated with surgical excision. Conclusion: Intracardiac thrombus is a rare but serious complication of nephrotic syndrome in children, especially in children with risk factors for potentially increased intracardiac thrombogenicity such as history of recent intervention.

Oral Health Status of Down's Syndrome Patients in Aseer, Saudi Arabia.

INTRODUCTION: It was observed that compared to the general population, mentally challenged subjects have higher rates of poor oral hygiene. Gingivitis is a common finding with moderate or severe variety reported most commonly. Periodontal disease is the most significant oral health problem, which can even lead to mobility of tooth and tooth loss. It is essential to generate oral health precautionary agendas and familiarize it with them. MATERIALS AND METHODS: Four Centres of rehabilitation in the Aseer region of Saudi Arabia were included and subjects of 3-24 year age group was examined. Written informed consent was obtained from directors of Centres. Caries, oral hygiene status, soft-tissue and hard-tissue lesions were evaluated and statistically analyzed. RESULTS: Higher Decayed, Missing and Filled Teeth score was observed in the present study in contrast to many other studies. Periodontal disease was seen in higher age group with higher percentages. Poor oral hygiene was seen in 27.3% of males and 66.7% of females. Tongue lesions comprised 85.7%, which accounted for the major part of soft-tissue abnormality. Various occlusal abnormalities (75%) and developmental tooth disturbances (53.6%) constitute the higher portion in hard-tissue abnormality. CONCLUSION: Dentists should be conscious of the range of oral anomalies that can manifest in this group of the patients. The microbial or serological investigation was not done which could have explained the etiology behind these lesions. In spite of these confines, the survey result have provided information regarding the oral health status of Down's syndrome subjects and the necessity of focusing on oral health need.

Positional awake nasoendoscopic pattern-based surgical decision for correction of retropalatal obstruction in OSA.

PURPOSE: To evaluate differential surgical interventions for obstructive sleep apnea (OSA) patients with single-level retropalatal based on the preoperative topographical diagnosis using nasoendoscopy with Müller's maneuver during supine position (MM-P). SUBJECTS AND METHODS: This case series included adult patients with OSA who showed a predominant single-level retropalatal collapse on MM-P. An anteroposterior pattern of collapse was managed by an anterior advancement procedure, while a transverse pattern of collapse was managed by lateral/anterolateral advancement procedures (double suspension sutures). A combined procedure was provided to the concentric type of collapse. All patients underwent evaluation of the polysomnography, Epworth Sleepiness Scale (ESS) values and snoring scores both preoperatively and 6-8 months after surgery. RESULTS: Among 102 patients, the most commonly reported pattern of collapse at the retropalatal level was the concentric pattern (48.04%) followed by the transverse pattern (27.45%). The AP-pattern of collapse was reported in 24.51%. In the postoperative follow-up visits, no early or late complications were reported. All included groups showed significant improvement in polysomnographic data (mean AHI and lowest O2 saturation level). Significant improvement of VAS of snoring was reported. The overall success rate was ˃90%. CONCLUSION: Preoperative differential diagnosis of OSA with MM-P allows for tailored surgical management. Tailored procedures could yield good surgical outcomes when patients are properly selected and the technique is chosen according to preoperative topographical diagnostic assessment. This study might provide an available less-costly and effective preoperative planning for OSA intervention. LEVEL OF EVIDENCE: 4.

Faculty Members' Attitude Toward Establishing a Business Center in the Dental School Clinic, King Saud University.

AIMS AND OBJECTIVES: The aim of this study was to evaluate the faculty members' attitude toward establishing a business center in the College of Dentistry, King Saud University, Riyadh, Saudi Arabia. MATERIALS AND METHODS: A structured questionnaire comprised three sections: demographic variables such as gender and age, questions regarding the faculty's attitude toward a dental college business center, and a comparison between a business center and private clinics. Statistical analysis was performed to compare the groups with scale response variable; independent t-test, analysis of variance, and factor analysis were also conducted. RESULTS: The study results show that 43 respondents were men and 26 were women. Twenty respondents were ranked in the position of assistant professor, whereas 38 were ranked as associate professors, and 11 as professors. In total, 88.5% of female faculty members supported the idea of establishing a business center at the dental college, whereas 65% of male faculty members supported the same idea. There is a statistically significant difference among them (P < 0.05). The factor analysis extracted three factors named as "treatment accessibility," "treatment effectiveness," and "treatment cost." The factor loading ranges from 0.652 to 0.867, and altogether they explain 79% of the total data variance. CONCLUSION: Our study proved that 65.1% of male and 88.5% of female dental faculty of the college supported the idea of establishing a business center at the dental college as funding is needed to invest in dental education and research programs in order to formulate the next generation of practitioners to serve the population. This study will add to the progress of establishing a business center in dental education and provides the patient with a treatment accessibility and effectiveness in a low-cost budget.

A comparative evaluation of the effect of phototherapy of fiber post on its bond strength to dental composite.

The aim of the study was to evaluate the effect of diode laser as a phototherapy on surface treatment of fiber post and its bond strength to resin core build-up material. In this in-vitro study fifty fiber post of 1 mm tip-diameter and 20 mm length were used. The posts were mounted and randomly divided into 5 groups (n = 10) according to different surface treatment methods i.e. Group 1: Control, Group 2: Application of 37% Phosphoric Acid, Group 3: Treated with 40% H2O2, Group 4: Sandblasted with 50 µm aluminium oxide particles and Group 5: treated with diode laser. In all specimens including control, a cylinder of resin composite around the fiber post was made. All Specimens were mounted in a universal testing machine. Push-out test was performed and bond strengths were calculated. Using analysis of variance (ANOVA) and post hoc Tukey multiple comparisons test, means of push out bond strength were compared among the study groups. The highest bond strength was shown among Sandblasted [50 µm, Aluminium Oxide (Al2O3)] group [139.86] and the lowest push-out bond strength was observed in Control specimens [75.73]. The bond strength values of fiber post treated with Diode laser [100.81] and 40% H2O2 [105.64] were comparable.

Evaluation of interleukin-18 in children with steroid-sensitive nephrotic syndrome before and after using levamisole.

Levamisole is often discussed as the first alternative to steroids. It is an antihelminthic drug that has been used for steroid-sensitive nephrotic syndrome (SSNS) for more than 20 years. Interleukin (IL)-18, a member of the IL-1 cytokine superfamily, is recognized as an important regulator of immune responses. The aim of the study was to investigate the IL-18 levels in serum from children with SSNS during relapse and remission after using levamisole or three months in a trial to test the efficacy of its action in reducing frequency of relapses in SSNS. This study was done on 23 children with frequently relapsing SSNS treated with levamisole besides steroids, then followed up three months; 16 males and seven females (mean age: 7.96 years and median 8 years). Clinical and laboratory assessments were done before starting therapy and after three months including cumulative dose of steroids and serum IL-18. We found that IL-18 level showed a significant elevation after three months of levamisole therapy compared to its level before initiation of levamisole therapy, with no relapses in these three months, no reported side effect, and significant reduction of cumulative dose of steroids. Levamisole effectiveness in reduction of relapses of SSNS may be due to resetting of the type 1/type 2 imbalance, proved by induction of IL 18 may be useful in the therapy.

Cerebral metabolic alterations and cognitive dysfunction in children with chronic kidney disease using magnetic resonance spectroscopy and Wechsler intelligence scale.

AIM: Many studies have described impaired intelligence, attention, memory and executive function in patients with chronic kidney disease (CKD) dialyzed and non-dialyzed, but there is still a lack of sensitive and early methods of detection of these deficits. The purpose of this study was to investigate the relationship between brain metabolic alteration [measured by magnetic resonance spectroscopy (MRS)] and cognitive dysfunction in CKD children (detected by psychometric analysis). METHODS: One hundred and forty patients with CKD were included (40 patients with stage 5 CKD on dialysis, 30 patients with stage 4 to 5 CKD without dialysis, and 70 patients with stage 1 to 3 CKD). All patients with previous neurological disorders were excluded. Conventional MRI, MRS and psychometric assessment using Wechsler intelligence scale for children third edition was done in all participants. RESULTS: We found a significant negative correlation between MRS abnormalities and Wechsler IQ Test scores. But there was a significantly positive correlation between the CKD stages and MRS abnormalities in patients with CKD and negative significant correlation between CKD stages and Wechsler IQ test scores in patients with CKD. There were correlations between "the electrolyte disturbance, blood haemoglobin and hypertension" and "the CKD staging, cognitive functions IQ scores and MRS parameter changes". CONCLUSION: It was concluded that both MRS and psychometric tests are sensitive methods for detection of impaired cognitive function in CKD children, particularly in the dialyzed group, and these findings appear before a clinical diagnosis.

Nasalance Changes Following Various Endonasal Surgeries

Abstract Introduction There is change in nasalance post endonasal surgery which is not permanent. Objectives The objective of this study is to evaluate the long-term nasalance changes following different types of endonasal surgeries. Methods We included in this study patients who underwent sinonasal surgery at the Otorhinolaryngology Department in Zagazig University Hospitals from February 2015 until March 2016. We divided the patients into two groups according to the surgeries they underwent: Group (A) was the FESS group and group (B), the septoturbinoplasty group.We checked nasalance using a nasometer before and after the sinonasal surgery. Results Nasalance increased at one month after the operation in both groups. However, it returned to nearly original levels within three months postoperatively. Conclusion FESS, septoplasty, and turbinate surgery may lead to hypernasal speech. This hypernasal speech can be a result of change in the shape and diameter of the resonating vocal tract. Hypernasal speech in these circumstances may be a temporary finding that can decrease with time. Surgeons should inform their patients about the possibility of hypernasality after such types of surgery, especially if they are professional voice users.

Nasalance Changes Following Various Endonasal Surgeries.

Introduction There is change in nasalance post endonasal surgery which is not permanent. Objectives The objective of this study is to evaluate the long-term nasalance changes following different types of endonasal surgeries. Methods We included in this study patients who underwent sinonasal surgery at the Otorhinolaryngology Department in Zagazig University Hospitals from February 2015 until March 2016. We divided the patients into two groups according to the surgeries they underwent: Group (A) was the FESS group and group (B), the septoturbinoplasty group. We checked nasalance using a nasometer before and after the sinonasal surgery. Results Nasalance increased at one month after the operation in both groups. However, it returned to nearly original levels within three months postoperatively. Conclusion FESS, septoplasty, and turbinate surgery may lead to hypernasal speech. This hypernasal speech can be a result of change in the shape and diameter of the resonating vocal tract. Hypernasal speech in these circumstances may be a temporary finding that can decrease with time. Surgeons should inform their patients about the possibility of hypernasality after such types of surgery, especially if they are professional voice users.