ABSTRACT
Endotracheal
mouth/nose
breathing
need
10%
this
to
performing
subjective
poorly
standard Unfortunately, these airway examination systems in
clinical practice perform only modestly, with sensitivities of 20-62%, specificities of 82-97%, and very low
positive predictive values, generally less than 30%, unless very liberal definitions of difficulty are used. There
are likely a number of reasons for this poor performance, including the relative rarity of difficult intubation, the
multifactorial etiology and varying definition of difficult intubation, inter-observer variability in test results, failure
to validate potential systems in patients independent of those used to derive the test, and the inadequacy of
the tests themselves.
intubation (EI) is a common medical procedure in which a plastic tube is introduced via the
into the trachea, to provide respiratory support during general anesthesia or to ameliorate
difficulty in cases of respiratory failure, cardiac arrest, or other forms of critical illness. The global
for EI is likely at least 150 million based on the WHO estimate of surgical need worldwide. Approximately
of EI attempts are difficult, and approximately 1/2000 are deemed impossible. The clinica l significance of
“can't intubate, can't ventilate” scenario is extremely important: 25% of anesthetic related deaths are due
airway mishaps. Patients are typically assessed for anatomic features that might predict difficulty in
EI prior to the procedure. In practice, anesthesiologists and other airway experts likely weigh other
factors in anticipating a difficult airway, including habitus, facial appearance, and perhaps other
understood hunches. The use of this examination to predict difficult intubation is considered the
of care in modern anesthesiology practice.
When
personnel
Conversely,
not
learning
and
intubation.
identify
accuracy
(Mallampati
anesthesiologists
reduce
mobilization
difficulty the airway is anticipated, more advanced techniques may be employed, additional
may be recruited for assistance, surgical airway expertise (i.e., tracheostomy) may be on standby
these techniques are expensive, time consuming, and uncomfortable to patients, so they should
be overused. We hypothesize that anesthesiologists' visual assessment can be modeled through deep
to identify patients with difficult intubation with high accuracy. Through innovative use of deep learning
sophisticated image analysis, this research will identify facial features tha accurately predict difficult
The research will utilize frontal as well as profile facial photographs to build a generative model to
difficult intubation patients. The developed model will be subjected to rigorous statistical analysis for
and reproducibility . In a clinical trial, the proposed model will be compared against the bedside tests
+ thryomental distance). The project will 1) result in innovative software tools to facilitate
and 2) substantially reduce unnecessary healthcare expenses. We expect that this model will
the probability of an unexpected difficult intubation and allow anesthesiologists to better prepare by
of alternative techniques, equipment, or operators.
with
.
t
