There are two I know of.
Picking the point where $TPR+FPR=1$, i.e. the point that lies on the "main" diagonal of the ROC curve.
Picking the point where $TPR+1-FPR$ is maximal.
Both are discussed in (Lobo, Jiménez‐Valverde & Real, 2008), which is also a good reference on misuses and misunderstandings on the AUC.
Paradoxically, once the AUC score was developed as a
threshold-independent measure, researchers proposed methods for
selecting a threshold from this curve. It has been assumed that in ROC
plots the optimal classifier point is the one that maximizes the sum
of sensitivity and specificity (Zweig & Campbell, 1993). However,
Jiménez-Valverde & Lobo (2007) have found that a threshold that
minimizes the difference between sensitivity and specificity performs
slightly better than one that maximizes the sum if commission and
omission errors are equally costly. When the threshold changes from 0
to 1, the rate of well-predicted presences diminishes while the rate
of well-predicted absences increases. The point where both curves
cross can be considered the appropriate threshold if both types of
errors are equally weighted (Fig. 1a). In a ROC plot, this point lies
at the intersection of the ROC curve and the line perpendicular to the
diagonal of no discrimination (Fig. 1b), i.e., the ‘northwesternmost’
point of the ROC curve. The two thresholds can be easily computed
without using the ROC curve. Both thresholds are highly correlated
and, more importantly, they also correlate with prevalence (Liu et
al., 2005; Jiménez-Valverde & Lobo, 2007). As a general rule, a good
classifier needs to minimize the false positive and negative rates or,
similarly, to maximize the true negative and positive rates. Thus, if
we place equal weight on presences and absences there is only one
correct threshold. This optimal threshold, the one that minimizes the
difference between sensitivity and specificity, achieves this
objective and provides a balanced trade-off between commission and
omission errors. Nevertheless, as pointed out before, if different
costs are assigned to false negatives and false positives, and the
prevalence bias is always taken into account, the threshold should be
selected according to the required criteria. It is also necessary to
underline that the transformation of continuous probabilities into
binary maps is frequently necessary for many practical applications
that rely on making decisions (e.g., reserve selection).
Lobo, J. M., Jiménez‐Valverde, A., & Real, R. (2008). AUC: a misleading measure of the performance of predictive distribution models. Global ecology and Biogeography, 17(2), 145-151.