No abstract

ELL PALSY OR IDIOPATHIC FAcial paralysis, an acute weakness or paralysis of the facial nerve, has a lifetime risk of 1 in 60. 1 The annual incidence of Bell palsy is 20 to 30 per 100 000 population. 2 While 71% of untreated patients will completely recover and 84% will have complete or near normal recovery, 3 the remainder will have persistent moderate to severe weakness, facial contracture, or synkinesis. 1 Initial severity is associated with a poor prognosis with as few as 61% of cases of complete pareses and as many as 94% of cases of incomplete pareses having complete recovery, 3 usually within 4 months of presentation. 3,4 A herpes infection likely causes this disorder. [5][6][7][8][9] Swelling of the nerve at the meatal foramen has been observed intraoperatively, 10 and sampling of endoneurial fluid during nerve decompression for Bell palsy has yielded DNA of herpes sim-

1b. Laryngoscope, 127:S4-S11, 2017.

One of the key challenges with big data is leveraging the complex network of information to yield useful clinical insights. The confluence of massive amounts of health data and a desire to make inferences and insights on these data has produced a substantial amount of interest in machine-learning analytic methods. There has been a drastic increase in the otolaryngology literature volume describing novel applications of machine learning within the past 5 years. In this timely contemporary review, we provide an overview of popular machine-learning techniques, and review recent machine-learning applications in otolaryngology-head and neck surgery including neurotology, head and neck oncology, laryngology, and rhinology. Investigators have realized significant success in validated models with model sensitivities and specificities approaching 100%. Challenges remain in the implementation of machine-learning algorithms. This may be in part the unfamiliarity of these techniques to clinician leaders on the front lines of patient care. Spreading awareness and confidence in machine learning will follow with further validation and proof-of-value analyses that demonstrate model performance superiority over established methods. We are poised to see a greater influx of machine-learning applications to clinical problems in otolaryngology-head and neck surgery, and it is prudent for providers to understand the potential benefits and limitations of these technologies.

The rate of CSF leakage after TL and RS procedures has remained stable. Factors influencing its occurrence include tumor size but not surgical approach. The TL-related leaks had a significantly higher surgical repair rate than RS-related leaks, an additional factor to consider when choosing an approach. The problem of CSF leakage becomes increasingly important as nonsurgical treatments for acoustic neuroma are developed.

Cochlear implants have a significant suppressive effect on tinnitus in 66% of implant users. Although the reduction in the subjectively perceived tinnitus was statistically significant, it did not correlate with HINT; however, it did correlate with three quality-of-life domains, more significantly for those whose pretreatment conditions were moderate or worse.

Facial nerve schwannomas are extremely slow growing and frequently present without facial dysfunction. It is possible to surgically remove these tumors while sparing the nerve; as a result, postoperative function is correspondingly better. Finally, the decision on how to treat these patients should be individualized and based on initial facial function, growth rate, surgical experience, and informed patient consent.

2C.