It is known that culture media (CM) promotes cellular growth, adhesion, and protects explanted primary brain cells from in vitro stresses. The fetal bovine serum (FBS) supplement used in most CM, however, contains significant quantities of extracellular vesicles (EVs) that confound quantitative and qualitative analyses from the EVs produced by the cultured cells. We quantitatively tested the ability of common FBS EV-depletion protocols to remove exogenous EVs from FBS-supplemented CM and evaluated the influence such methods have on primary astrocyte culture growth and viability. We assessed two methodologies utilized for FBS EV removal prior to adding to CM: (1) an 18-h ultracentrifugation (UC); and (2) a commercial EV-depleted FBS (Exo-FBS™). Our analysis demonstrated that Exo-FBS™ CM provided the largest depletion (75%) of total FBS EVs, while still providing 6.92 × 10⁸ ± 1.39 × 10⁸ EVs/mL. In addition, both UC and Exo-FBS™ CM resulted in poor primary astrocyte cell growth and viability in culture. The two common FBS EV-depletion methods investigated, therefore, not only contaminate in vitro primary cell-derived EV analyses, but also provide a suboptimal environment for primary astrocyte cell growth and viability. It appears likely that future CM optimization, using a serum-free alternative, might be required to advance analyses of cell-specific EVs isolated in vitro.

Objectives

To compare two statistical models, namely logistic regression and artificial neural network (ANN), in prediction of vestibular schwannoma (VS) recurrence.

Methods

Seven hundred eighty-nine patients with VS diagnosis completed an online survey. Potential predictors for recurrence were derived from univariate analysis by reaching the cut off P value of .05. Those nine potential predictors were years since treatment, surgeon's specialty, resection amount, and having incomplete eye closure, dry eye, double vision, facial pain, seizure, and voice/swallowing problem as a complication following treatment. Multivariate binary logistic regression model was compared with a four-layer 9-5-10-1 feedforward backpropagation ANN for prediction of recurrence.

Results

The overall recurrence rate was 14.5%. Significant predictors of recurrence in the regression model were years since treatment and resection amount (both P < .001). The regression model did not show an acceptable performance (area under the curve [AUC] = 0.64; P = .27). The regression model's sensitivity and specificity were 44% and 69%, respectively and correctly classified 56% of cases. The ANN showed a superior performance compared to the regression model (AUC = 0.79; P = .001) with higher sensitivity (61%) and specificity (81%), and correctly classified 70% of cases.

Conclusion

The constructed ANN model was superior to logistic regression in predicting patient-answered VS recurrence in an anonymous survey with higher sensitivity and specificity. Since artificial intelligence tools such as neural networks can have higher predictive abilities compared to logistic regression models, continuous investigation into their utility as complementary clinical tools in predicting certain surgical outcomes is warranted.

Objectives

To develop a multiclass-classifier deep learning model and website for distinguishing tympanic membrane (TM) pathologies based on otoscopic images.

Methods

An otoscopic image database developed by utilizing publicly available online images and open databases was assessed by convolutional neural network (CNN) models including ResNet-50, Inception-V3, Inception-Resnet-V2, and MobileNetV2. Training and testing were conducted with a 75:25 breakdown. Area under the curve of receiver operating characteristics (AUC-ROC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to compare different CNN models' performances in classifying TM images.

Results

Our database included 400 images, organized into normal (n = 196) and abnormal classes (n = 204), including acute otitis media (n = 116), otitis externa (n = 44), chronic suppurative otitis media (n = 23), and cerumen impaction (n = 21). For binary classification between normal versus abnormal TM, the best performing model had average AUC-ROC of 0.902 (MobileNetV2), followed by 0.745 (Inception-Resnet-V2), 0.731 (ResNet-50), and 0.636 (Inception-V3). Accuracy ranged between 0.73-0.77, sensitivity 0.72-0.88, specificity 0.58-0.84, PPV 0.68-0.81, and NPV 0.73-0.83. Macro-AUC-ROC for MobileNetV2 based multiclass-classifier was 0.91, with accuracy of 66%. Binary and multiclass-classifier models based on MobileNetV2 were loaded onto a publicly accessible and user-friendly website (https://headneckml.com/tympanic). This allows the readership to upload TM images for real-time predictions using the developed algorithms.

Conclusions

Novel CNN algorithms were developed with high AUC-ROCs for differentiating between various TM pathologies. This was further deployed as a proof-of-concept publicly accessible website for real-time predictions.

Objectives

To compare two statistical models, namely logistic regression and artificial neural network (ANN), in prediction of vestibular schwannoma (VS) recurrence.

Methods

Seven hundred eighty-nine patients with VS diagnosis completed an online survey. Potential predictors for recurrence were derived from univariate analysis by reaching the cut off P value of .05. Those nine potential predictors were years since treatment, surgeon's specialty, resection amount, and having incomplete eye closure, dry eye, double vision, facial pain, seizure, and voice/swallowing problem as a complication following treatment. Multivariate binary logistic regression model was compared with a four-layer 9-5-10-1 feedforward backpropagation ANN for prediction of recurrence.

Results

The overall recurrence rate was 14.5%. Significant predictors of recurrence in the regression model were years since treatment and resection amount (both P < .001). The regression model did not show an acceptable performance (area under the curve [AUC] = 0.64; P = .27). The regression model's sensitivity and specificity were 44% and 69%, respectively and correctly classified 56% of cases. The ANN showed a superior performance compared to the regression model (AUC = 0.79; P = .001) with higher sensitivity (61%) and specificity (81%), and correctly classified 70% of cases.

Conclusion

The constructed ANN model was superior to logistic regression in predicting patient-answered VS recurrence in an anonymous survey with higher sensitivity and specificity. Since artificial intelligence tools such as neural networks can have higher predictive abilities compared to logistic regression models, continuous investigation into their utility as complementary clinical tools in predicting certain surgical outcomes is warranted.

OBJECTIVES:To analyze medical malpractice lawsuit trends pertaining to cases of vestibular schwannomas (VS). METHODS:Two major computerized legal databases (LexisNexis and WestLaw) were queried and reviewed for evaluation of all the US state and federal court records from civil trials alleging malpractice between 1976 and 2016. RESULTS:A total of 32 VS cases were identified. Allegations were divided into four categories: misdiagnosis/delayed diagnosis (47%), postoperative complications (44%), failure of informed consent or information sharing (16%), and other (3%). Postoperative complications included facial nerve paralysis, myocardial infarction, meningitis, and intracranial hemorrhage. Judgment amounts ranged from $300,000 to $2,000,000. The specialist type was specified for 24 of the 32 cases (75%): neurosurgeons (n = 9; 37%), neurotologists (n = 6; 25%), general otolaryngologists (n = 5; 21%), primary care physicians (n = 4; 17%), neurologists (n = 3; 12%), radiologists (n = 3; 12%), anesthesiologists (n = 2; 8%), radiation oncologists (n = 1; 4%), and general surgeon (n = 1; 4%). Of these 24 cases, (n = 9; 37%) two or more physicians were named as defendants in the lawsuit. CONCLUSIONS:Enhanced physician-patient communication, ensuring proper and adequate patient consent procedures, and proper documentation are good practices that may decrease the likelihood of lawsuits.

Retinal hemorrhages in pediatric patients can be a diagnostic challenge for ophthalmologists. These hemorrhages can occur due to various underlying etiologies, including abusive head trauma, accidental trauma, and medical conditions. Accurate identification of the etiology is crucial for appropriate management and legal considerations. In recent years, deep learning techniques have shown promise in assisting healthcare professionals in making more accurate and timely diagnosis of a variety of disorders. We explore the potential of deep learning approaches for differentiating etiologies of pediatric retinal hemorrhages. Our study, which spanned multiple centers, analyzed 898 images, resulting in a final dataset of 597 retinal hemorrhage fundus photos categorized into medical (49.9%) and trauma (50.1%) etiologies. Deep learning models, specifically those based on ResNet and transformer architectures, were applied; FastViT-SA12, a hybrid transformer model, achieved the highest accuracy (90.55%) and area under the receiver operating characteristic curve (AUC) of 90.55%, while ResNet18 secured the highest sensitivity value (96.77%) on an independent test dataset. The study highlighted areas for optimization in artificial intelligence (AI) models specifically for pediatric retinal hemorrhages. While AI proves valuable in diagnosing these hemorrhages, the expertise of medical professionals remains irreplaceable. Collaborative efforts between AI specialists and pediatric ophthalmologists are crucial to fully harness AIs potential in diagnosing etiologies of pediatric retinal hemorrhages.

OBJECTIVES: To analyze medical malpractice lawsuit trends pertaining to cases of vestibular schwannomas (VS). METHODS: Two major computerized legal databases (LexisNexis and WestLaw) were queried and reviewed for evaluation of all the US state and federal court records from civil trials alleging malpractice between 1976 and 2016. RESULTS: A total of 32 VS cases were identified. Allegations were divided into four categories: misdiagnosis/delayed diagnosis (47%), postoperative complications (44%), failure of informed consent or information sharing (16%), and other (3%). Postoperative complications included facial nerve paralysis, myocardial infarction, meningitis, and intracranial hemorrhage. Judgment amounts ranged from $300,000 to $2,000,000. The specialist type was specified for 24 of the 32 cases (75%): neurosurgeons (n = 9; 37%), neurotologists (n = 6; 25%), general otolaryngologists (n = 5; 21%), primary care physicians (n = 4; 17%), neurologists (n = 3; 12%), radiologists (n = 3; 12%), anesthesiologists (n = 2; 8%), radiation oncologists (n = 1; 4%), and general surgeon (n = 1; 4%). Of these 24 cases, (n = 9; 37%) two or more physicians were named as defendants in the lawsuit. CONCLUSIONS: Enhanced physician-patient communication, ensuring proper and adequate patient consent procedures, and proper documentation are good practices that may decrease the likelihood of lawsuits.

Objectives

To develop a smartphone application providing sound therapy and cognitive behavioral therapy (CBT) for treating tinnitus and performing a proof-of-concept pilot study evaluating its potential efficacy.

Methods

An interactive smartphone application available on iOS and Android platforms was developed, which provided an 8-week tinnitus-specific CBT and personalized and frequency-matched sound therapy. Included patients presented to our tertiary clinic between 2017 and 2018, while those waitlisted were regarded as controls. Three surveys were administrated: Tinnitus Handicap Inventory (THI), Generalized Anxiety Disorder 7-item (GAD-7), and Perceived Stress Scale (PSS).

Results

A total of 30 patients enrolled in this study consisting of 20 treatment and 10 control patients and mean age was 55.4 ± 11.6 years. Treatment and control patients had similar age, sex, and pre-enrolment GAD and PSS (all P > .05). Baseline THI scores were also similar between treatment and control cohorts (50.1 ± 21.9 vs 62.0 ± 20.7; P = .15). After 8 weeks, though changes in GAD and PSS scores were similar (P > .05), the treatment group reported a significantly greater improvement in THI scores (17.7 ± 15.8 vs 5.3 ± 10.5, P = .04).

Conclusions

This pilot study demonstrated potentially promising efficacy of a smartphone-based CBT and sound therapy platform for treating tinnitus and encourages future randomized controlled trials on this treatment modality.