The recurrence of cholesteatoma after surgical treatment often occurs as a result of poor mucosal regeneration in the middle ear cavity and mastoid cavity and changes, such as granulation tissue formation, which impair gas exchange in the middle ear cavity. Conventional tympanoplasty often results in a lack of mucosal regeneration in the resected area of the mastoid cavity. In particular, mucosal regeneration in a poorly pneumatized mastoid cavity is extremely difficult. If the middle ear mucosa can be preserved or rapid postoperative regeneration of mucosa on the exposed bone surface can be achieved after middle ear surgery, the results of surgical treatment for otitis media, including cholesteatoma, can potentially be improved and the physiological function of the middle ear can be recovered. To overcome these limitations, we developed a novel treatment method combining tympanoplasty and autologous nasal mucosal epithelial cell sheet transplantation for postoperative regeneration of the middle ear mucosa. In clinical research, we endoscopically removed an approximately 10 × 10 mm²piece of nasal mucosal tissue. Tissue-engineered autologous nasal mucosal epithelial cell sheets were fabricated by culturing the harvested cells in an aseptic environment in a good manufacturing practice-compliant cell processing facility. The cultivated cell sheets were transplanted, during tympanoplasty, onto the exposed bony surface of the attic of the tympanic and mastoid cavities where the mucosa had been lost. We performed this procedure on four patients with middle ear cholesteatoma and one patient with adhesive otitis media. All patients showed favorable postoperative course with no adverse events or complications and the patients’ hearing ability post-transplantation remained good.

Background: The indications for transcanal endoscopic ear surgery (TEES) for middle ear cholesteatoma have expanded for cases involving mastoid extension. However, TEES is not indicated for all cases with mastoid extension. In addition, predicting the extent of external auditory canal (EAC) removal needed for cholesteatoma resection is not always easy. The purpose of this study was to use augmented reality (AR) to project the lesion onto an intraoperative endoscopic image to predict EAC removal requirements and select an appropriate surgical approach. Methods: In this study, patients showing mastoid extension were operated on using a navigation system with an AR function (Stryker). Results: The results showed that some cases with lesions slightly extending into the antrum required extensive resection of the EAC, while cases with lesions extending throughout the antrum required smaller resection of the EAC, indicating TEES. Conclusions: By predicting the extent of the needed EAC removal, it is possible to determine whether TEES (a retrograde approach) or canal wall-up mastoidectomy, which preserves as much of the EAC as possible, should be performed. We believe that our findings will contribute to the success of middle ear surgeries and the implementation of robotic surgery in the future.

Objective: To examine the association of habitual sniffing with mastoid cell development and contralateral ear abnormalities in acquired pars flaccida cholesteatoma. Study design: Retrospective comparative study. Setting: Two tertiary referral hospitals. Patients: A total of 124 adults (133 ears) underwent primary surgery for acquired pars flaccida cholesteatoma between 2012 and 2026. Intervention(s): Primary cholesteatoma surgery. Main outcome measure(s): Mastoid cell development score classified according to the Japan Otological Society criteria and abnormal contralateral ear findings. Results: Habitual sniffing was identified in 67 of 133 ears (50.4%). The sniffing group had higher MC scores than the non-sniffing group, indicating better mastoid cell development (1.48±0.68 vs. 1.17±0.67; effect size r =0.222; P =0.010). Ordinal logistic regression showed that habitual sniffing was independently associated with better mastoid cell development [odds ratio (OR)=2.35; 95% confidence interval (CI), 1.15-4.87; P =0.020]. Among 110 ears with evaluable contralateral endoscopic findings, contralateral abnormalities were more frequent in the sniffing group than in the nonsniffing group (71.4% vs. 35.2%; OR=4.536; P =0.0002). Binary logistic regression showed that habitual sniffing was independently associated with abnormal contralateral ear findings (OR=4.57; 95% CI, 1.94-11.34; P =0.0007). Conclusions: Habitual sniffing was associated with better mastoid cell development and frequent contralateral ear abnormalities in acquired pars flaccida cholesteatoma, suggesting a distinct pathophysiologic subgroup related to dynamic negative pressure loading.

Surgical treatment is the only option for cholesteatoma; however, the recurrence rate is high, and the incidence of residual cholesteatoma recurrence largely depends on the surgeon's skill. Training deep neural network (DNN) models typically requires large datasets, but the prevalence of cholesteatoma is low (1 in 25,000 people). However, cholesteatoma remains difficult to treat. Developing analytical methods to improve ac-curacy with limited datasets remains a significant challenge in medical artificial intelli-gence (AI) research. This study introduces an AI-based system for detecting residual cholesteatoma in surgical field videos. A retrospective analysis was conducted on 144 cases from 88 patients who underwent surgery. The training dataset comprised videos of cholesteatoma lesions recorded during surgery and intact middle ear mucosa after lesion removal. These videos were captured using both endoscope and microscope for AI model development. The diagnostic accuracy was approximately 80% for both endoscopic and microscopic images. Although the diagnostic accuracy for microscopic images was slightly lower, focusing on the lesion center improved the accuracy to a level comparable to that of endoscopic images. This study demonstrates the diagnostic feasibility of AI-based cholesteatoma detection despite a limited sample size highlighting the value of proof-of-concept studies in clar-ifying technical requirements for future clinical systems and is the first AI study to use videos from both modalities.

Surgical treatment is the only option for cholesteatoma; however, the recurrence rate is high, and the incidence of residual cholesteatoma recurrence largely depends on the surgeon’s skill. Training deep neural network (DNN) models typically requires large datasets, but the prevalence of cholesteatoma is low (1 in 25,000 people). It also remains difficult to treat. Developing analytical methods to improve accuracy with limited datasets remains a significant challenge in medical artificial intelligence (AI) research. This study introduces an AI-based system for detecting residual cholesteatoma in surgical field videos. A retrospective analysis was conducted on 144 cases from 88 patients who underwent surgery. The training dataset comprised videos of cholesteatoma lesions recorded during surgery and intact middle ear mucosa after lesion removal. These videos were captured using both an endoscope and a surgical microscope for AI model development. The diagnostic accuracy was approximately 80% for both endoscopic and microscopic images. Although the diagnostic accuracy for microscopic images was slightly lower, focusing on the lesion center improved the accuracy to a level comparable to that of endoscopic images. This study demonstrates the diagnostic feasibility of AI-based cholesteatoma detection despite a limited sample size, highlighting the value of proof-of-concept studies in clarifying technical requirements for future clinical systems. To our knowledge, this is the first AI study to use videos from both modalities.