The aim of this study is to investigate the use of an exponential-plateau model to determine the required training dataset size that yields the maximum medical image segmentation performance. CT and MR images of patients with renal tumors acquired between 1997 and 2017 were retrospectively collected from our nephrectomy registry. Modality-based datasets of 50, 100, 150, 200, 250, and 300 images were assembled to train models with an 80–20 training-validation split evaluated against 50 randomly held out test set images. A third experiment using the KiTS21 dataset was also used to explore the effects of different model architectures. Exponential-plateau models were used to establish the relationship of dataset size to model generalizability performance. For segmenting non-neoplastic kidney regions on CT and MR imaging, our model yielded test Dice score plateaus of $$0.93\pm 0.02$$ 0.93 ± 0.02 and $$0.92\pm 0.04$$ 0.92 ± 0.04 with the number of training-validation images needed to reach the plateaus of 54 and 122, respectively. For segmenting CT and MR tumor regions, we modeled a test Dice score plateau of $$0.85\pm 0.20$$ 0.85 ± 0.20 and $$0.76\pm 0.27$$ 0.76 ± 0.27 , with 125 and 389 training-validation images needed to reach the plateaus. For the KiTS21 dataset, the best Dice score plateaus for nn-UNet 2D and 3D architectures were $$0.67\pm 0.29$$ 0.67 ± 0.29 and $$0.84\pm 0.18$$ 0.84 ± 0.18 with number to reach performance plateau of 177 and 440. Our research validates that differing imaging modalities, target structures, and model architectures all affect the amount of training images required to reach a performance plateau. The modeling approach we developed will help future researchers determine for their experiments when additional training-validation images will likely not further improve model performance.

Objective To examine the utility of telemedicine in a tertiary otologic practice. Study Design Retrospective case series. Setting Tertiary neurotology clinic. Patients Consecutive adult patients presenting via video visit between January 2020 and January 2021. Intervention(s) Televideo modality to conduct visits with patients seeking evaluation for new concerns, second opinions, or routine follow-up for established conditions. Main Outcome Measure(s) Success of the televideo visit defined by the televideo visit being sufficient for determining a definitive plan and not requiring deferment of recommendations for a subsequent in-person visit. Results A total of 102 televideo visits were performed among 100 unique patients. Of those, 92 (90.2%) visits were for second opinions or evaluation of new concerns, most commonly for vestibular schwannoma (n = 32, 31.4%), followed by sensorineural hearing loss (n = 20, 19.6%). Other visits were conducted for early postoperative follow-up and established general follow-up. In 91.2% of cases (n = 93), patients were successfully evaluated and provided recommendations from the initial video visit. All visits with patients having a diagnosis of meningioma (n = 7), and nearly all with vestibular Schwannoma (97%, n = 31) and sensorineural hearing loss (95%, n = 19) were successful. Of the 79 patients offered surgery as one potential treatment option, 31 patients underwent surgery at our institution by time of review. Patients with unsuccessful visits (n = 9, 8.8%) were advised to schedule additional in-person diagnostic imaging, vestibular testing, or cochlear implant candidacy evaluation to establish a more definitive care plan. Conclusion Virtual televideo visits were successful for a high percentage of selected patients seen at a tertiary neurotology practice, particularly those seeking evaluation of vestibular schwannoma or sensorineural hearing loss.

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