Internal auditory canal (IAC) and cerebellopontine metastases are very rare lesions and their diagnosis is very difficult due to their similarity to vestibular schwannoma in clinical characteristics and radiologic findings. Our case is peculiar and differs from previously reported cases of malignant metastasis to the IAC in two aspects: 1) solitary IAC metastasis occurred without distant metastasis and 2) symptoms due to IAC metastasis preceded outbreak of primary lesion symptoms or diagnosis of primary lesion. In our literature search, no correlating cases have been reported. In this peculiar case, rapid progression of otologic symptoms, short duration of onset from hearing impairment to facial palsy, and laterally eccentric contrast enhancement in gadolinium-enhanced T1-weighted magnetic resonance imaging may have been clues for metastatic lesion.

There are various causes of facial canal enlargement. From congenital anomalies to neoplasms, many pathologic conditions should be considered. However, normal variants of vascular anatomy can also result in facial canal enlargement on temporal bone imaging. In order to avoid unnecessary procedures and complications, such as bleeding and facial nerve injury, this possibility should also be taken into account. A case of a 50-year-old male who was diagnosed with facial canal enlargement because of venous engorgement in the facial canal is herein described.

In high-aspect ratio laser drilling, many laser and optical parameters can be controlled, including the high-laser beam fluence and number of drilling process cycles. Measurement of the drilled hole depth is occasionally difficult or time consuming, especially during machining processes. This study aimed to estimate the drilled hole depth in high-aspect ratio laser drilling by using captured two-dimensional (2D) hole images. The measuring conditions included light brightness, light exposure time, and gamma value. In this study, a method for predicting the depth of a machined hole by using a deep learning methodology was devised. Adjusting the laser power and the number of processing cycles for blind hole generation and image analysis yielded optimal conditions. Furthermore, to forecast the form of the machined hole, we identified the best circumstances based on changes in the exposure duration and gamma value of the microscope, which is a 2D image measurement instrument. After extracting the data frame by detecting the contrast data of the hole by using an interferometer, the hole depth was predicted using a deep neural network with a precision of within 5 μm for a hole within 100 μm.

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