BiZact-Assisted Endoscopic Resection of a Sinonasal Tumor.

The BiZact device, a bipolar electrosurgical scissor designed for tonsillectomy, minimizes thermal tissue damage and seals blood vessels <3 mm in diameter while dividing the soft tissue. This study describes the authors' experience with sinonasal tumor surgery using a BiZact and discusses its clinical utility and advantages. The authors analyzed BiZact-assisted endoscopic sinonasal tumor surgery cases between January 2021 and May 2023. Data were collected on patients' demographics, histopathology, extent of tumor involvement, surgical records, and postoperative medical records. Clinical utility was assessed using the success rate of complete tumor excision, estimated blood loss during surgery, device-related complications, and operation time. A survey of the surgeons' BiZact experience was also conducted. The diagnoses of the 20 patients in this study included squamous cell carcinoma (n = 2), malignant melanoma (n = 1), sarcoma (n = 1), natural killer cell lymphoma (n = 1), inverted papilloma (n = 12), angiofibroma (n = 2), and schwannoma (n = 1). This pilot study demonstrated a shortened operative time, with a median of 0.8 hours and <100 mL of intraoperative blood loss. In addition, no BiZact-related complications were observed. The BiZact device allows efficient sinonasal surgery because it has the unique advantage of one-step sealing and cutting. BiZact-assisted endoscopic sinonasal tumor surgery is a beneficial and safe procedure that reduces blood loss during surgery, shortens the operative time, and minimizes postoperative complications.

Automated Detection of Maxillary Sinus Opacifications Compatible with Sinusitis from CT Images.

BACKGROUND: Sinusitis is a commonly encountered clinical condition that imposes a considerable burden on the healthcare systems. A significant number of maxillary sinus opacifications are diagnosed as sinusitis, often overlooking the precise differentiation between cystic formations and inflammatory sinusitis, resulting in inappropriate clinical treatment. This study aims to improve diagnostic accuracy by investigating the feasibility of differentiating maxillary sinusitis, retention cysts, and normal sinuses. METHODS: We developed a deep learning-based automatic detection model to diagnose maxillary sinusitis using ostiomeatal unit computed tomography images. Of the 1080 randomly selected coronal-view CT images, including 2158 maxillary sinuses, datasets of maxillary sinus lesions comprised 1138 normal sinuses, 366 cysts, and 654 sinusitis based on radiographic findings, and were divided into training (n = 648 CT images), validation (n = 216), and test (n = 216) sets. We utilized a You Only Look Once based model for object detection, enhanced by the transfer learning method. To address the insufficiency of training data, various data augmentation techniques were adopted, thereby improving the model's robustness. RESULTS: The trained You Only Look Once version 8 nano (YOLOv8n) model achieved an overall precision of 97.1%, with the following class precisions on the test set: normal = 96.9%, cyst = 95.2%, and sinusitis = 99.2%. With an average F1 score of 95.4%, the F1 score was the highest for normal, then sinusitis, and finally, cysts. Upon evaluating a performance on difficulty level, the precision decreased to 92.4% on challenging test dataset. CONCLUSIONS: The developed model is feasible for assisting clinicians in screening maxillary sinusitis lesions.

Use of Perpendicular Plate of Ethmoid Split Technique in Rhinoplasty and Septoplasty.

In recent years, the perpendicular plate of ethmoid (PPE) has become a prominent choice for bone-grafting materials. This study introduces the PPE split technique for efficient removal of the bony septum or the harvesting of partial PPE for septal batten grafts in rhinoplasty and septoplasty, particularly suited for cases with thick PPE. Herein, this technique was employed to minimize septal defects and prevent complications while achieving harvested bony grafts of the desired effective thickness. In this retrospective study, 36 patients underwent surgery using the PPE split technique (22 septoplasties and 14 rhinoplasties). The procedure involved detaching the PPE from the septal cartilage and performing a vertical plane osteotomy with a number 10 scalpel blade to remove bony spurs or prepare batten grafts. Paranasal sinus CT revealed that the average anteroinferior PPE thickness was 3.46 mm. This technique was used for both bony batten graft harvesting (26 cases) and bony spur removal, and septum reshaping (10 cases). Successful PPE harvesting was achieved in all cases except for three, which experienced graft breakage. This study demonstrates the viability and effectiveness of the PPE split technique as a supplementary maneuver in septoplasty and rhinoplasty procedures. Its diverse applications include reshaping, correcting deviated septum, harvesting bony batten grafts, and providing support for an aesthetically pleasing nasal profile.

Diagnostic accuracy of clinical visual assessment using endoscopic images for nasal cavity mass lesions.

Objectives: Discrimination of nasal cavity lesions using nasal endoscopy is challenging because of the differences in clinical manifestations and treatment strategies. We aimed to investigate the diagnostic accuracy of clinical visual assessment (CVA) of nasal cavity masses using endoscopic images and determine whether there is a difference according to pathologic class and the examiners' experience. Methods: We collected pathologically confirmed endoscopic images of normal findings, nasal polyp (NP), benign tumor, and malignant tumor (each class contained 100 images) randomly selected. Eighteen otolaryngologists, including six junior residents, six senior residents, and six board-certified rhinologists classified the test set images into four classes of lesions by CVA. Diagnostic performance according to the pathologic class and the examiner's experience level was evaluated based on overall accuracy, F1-score, confusion matrix, and area under the receiver operating characteristic curve (AUC). Results: Diagnostic performance was significantly different according to the pathological class of nasal cavity mass lesions with the overall accuracy reported high in the order of normal, NP, benign tumor, and malignant tumor (0.926 ± 0.100; 0.819 ± 0.135; 0.580 ± 0.112; 0.478 ± 0.187, respectively), F1 score (0.937 ± 0.076; 0.730 ± 0.093; 0.549 ± 0.080; 0.554 ± 0.146, respectively) and AUC value (0.96 ± 0.06; 0.84 ± 0.07; 0.70 ± 0.05; 0.71 ± 0.08, respectively). The expert rhinologist group achieved higher overall accuracy than the resident group (0.756 ± 0.157 vs. 0.680 ± 0.239, p < .05). Conclusion: CVA for nasal cavity mass was highly dependent on the pathologic class and examiner's experience. The overall accuracy was reliably high for normal findings, but low in classifying benign and malignant tumors. Differential diagnosis of lesions solely based on nasal endoscopic evaluation is challenging. Therefore, clinicians should consider further clinical evaluation for suspicious cases.

Retrospective comparison of first-line treatments for odontogenic sinusitis based on duration of symptoms.

Objectives: To investigate the clinical efficacy of dental treatment and endoscopic sinus surgery (ESS), each primary/combined treatment modality, in patients with odontogenic sinusitis (ODS), according to its phase, acute or chronic. Materials and methods: We retrospectively reviewed clinical data on 172 patients diagnosed with ODS. They were divided into two groups: acute (≤ 3 months; 90 patients) and chronic (> 3 months; 82 patients) ODS. The success rate and time to resolution of each primary/combined treatment modality were compared between the two groups. Results: In both ODS groups, the success rate was highest with combined ESS and dental therapy, followed by ESS alone and dental therapy alone. ESS outperformed dental therapy (96.6% vs 65.5% for acute ODS, p = 0.011; 80.6% vs 56.5% for chronic ODS, p = 0.046) and led to quicker resolution of symptoms for acute ODS than dental therapy (0.9 vs 1.7 months, p = 0.012). In the comparison between ESS alone and combined therapy, no significant difference was observed for acute ODS, whereas combined therapy demonstrated a superior success rate for chronic ODS (100% vs 80.6%, p = 0.046). Conclusions: In our study, the clinical utility of dental treatment and/or ESS depended on the morbidity period of ODS. For chronic ODS, combined ESS and dental treatment seems to be an effective first-line treatment.

Impact of nasal septal perforation on the airflow and air-conditioning characteristics of the nasal cavity.

We investigated (1) how nasal septal perforations (NSPs) modify nasal airflow and air-conditioning characteristics and (2) how the modifications of nasal airflow are influenced by the size and location of the NSP. Computed tomography scans of 14 subjects with NSPs were used to generate nasal cavity models. Virtual repair of NSPs was conducted to examine the sole effect of NSPs on airflow. The computational fluid dynamics technique was used to assess geometric and airflow parameters around the NSPs and in the nasopharynx. The net crossover airflow rate, the increased wall shear stress (WSS) and the surface water-vapor flux on the posterior surface of the NSPs were not correlated with the size of the perforation. After the virtual closure of the NSPs, the levels in relative humidity (RH), air temperature (AT) and nasal resistance did not improve significantly both in the choanae and nasopharynx. A geometric parameter associated with turbinate volume, the surface area-to-volume ratio (SAVR), was shown to be an important factor in the determination of the RH and AT, even in the presence of NSPs. The levels of RH and AT in the choanae and nasopharynx were more influenced by SAVR than the size and location of the NSPs.

Deep learning algorithm for the automated detection and classification of nasal cavity mass in nasal endoscopic images.

Nasal endoscopy is routinely performed to distinguish the pathological types of masses. There is a lack of studies on deep learning algorithms for discriminating a wide range of endoscopic nasal cavity mass lesions. Therefore, we aimed to develop an endoscopic-examination-based deep learning model to detect and classify nasal cavity mass lesions, including nasal polyps (NPs), benign tumors, and malignant tumors. The clinical feasibility of the model was evaluated by comparing the results to those of manual assessment. Biopsy-confirmed nasal endoscopic images were obtained from 17 hospitals in South Korea. Here, 400 images were used for the test set. The training and validation datasets consisted of 149,043 normal nasal cavity, 311,043 NP, 9,271 benign tumor, and 5,323 malignant tumor lesion images. The proposed Xception architecture achieved an overall accuracy of 0.792 with the following class accuracies on the test set: normal = 0.978 ± 0.016, NP = 0.790 ± 0.016, benign = 0.708 ± 0.100, and malignant = 0.698 ± 0.116. With an average area under the receiver operating characteristic curve (AUC) of 0.947, the AUC values and F1 score were highest in the order of normal, NP, malignant tumor, and benign tumor classes. The classification performances of the proposed model were comparable with those of manual assessment in the normal and NP classes. The proposed model outperformed manual assessment in the benign and malignant tumor classes (sensitivities of 0.708 ± 0.100 vs. 0.549 ± 0.172, 0.698 ± 0.116 vs. 0.518 ± 0.153, respectively). In urgent (malignant) versus nonurgent binary predictions, the deep learning model achieved superior diagnostic accuracy. The developed model based on endoscopic images achieved satisfactory performance in classifying four classes of nasal cavity mass lesions, namely normal, NP, benign tumor, and malignant tumor. The developed model can therefore be used to screen nasal cavity lesions accurately and rapidly.