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The potential benefits to surgical outcomes of intraoperative and/or same-day computed tomography (CT) during isolated orbital fracture reconstruction are debatable, and previous research on this topic is limited by small sample size. This retrospective IBM MarketScan Commercial and Medicare Supplemental research database study examined patients undergoing isolated orbital reconstruction from January 1, 2012 to December 31, 2018, to assess whether same-day CT affected postoperative outcomes. The average age of the 5,023 participants was 37 (standard deviation [SD]: 16) years and 63% were males. The data revealed that 16.2% (815 of 5,023) patients underwent a same-day CT. Those who underwent a same-day CT scan exhibited reduced odds of postoperative enophthalmos (adjusted odds ratio [aOR]: 0.269; 95% confidence interval [CI]: 0.167-0.433) and diplopia (aOR: 0.670; 95% CI: 0.495-906). Interestingly, these patients also displayed a higher rate of revision surgeries (aOR: 2.721; 95% CI: 1.893-3.912). In summary, while same-day CT scans diminish certain postoperative complications of orbital fracture repair, they are also associated with an increased likelihood of subsequent surgical revision., Competing Interests: None declared., (Thieme. All rights reserved.)

Clinical applications of artificial intelligence (AI) have grown exponentially with increasing computational power and Big Data. Data rich fields such as Otology and Neurotology are still in the infancy of harnessing the power of AI but are increasingly involved in training and developing ways to incorporate AI into patient care. Current studies involving AI are focused on accessible datasets; health care wearables, tabular data from electronic medical records, electrophysiologic measurements, imaging, and "omics" provide huge amounts of data to utilize. Health care wearables, such as hearing aids and cochlear implants, are a ripe environment for AI implementation., Competing Interests: Disclosure N. Rapoport, C. Pavelchek, and A.P. Michelson have no conflicts of interest to disclose. M.A. Shew – consultant for Cochlear Ltd; Triological Society Research Career Development Award., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: To address outcome heterogeneity in cochlear implant (CI) research, we built imputation models using multiple imputation by chained equations (MICEs) and K-nearest neighbors (KNNs) to convert between four common open-set testing scenarios: Consonant-Nucleus-Consonant word (CNCw), Arizona Biomedical (AzBio) in quiet, AzBio +5, and AzBio +10. We then analyzed raw and imputed data sets to evaluate factors affecting CI outcome variability., Study Design: Retrospective cohort study of a national CI database (HERMES) and a nonoverlapping single-institution CI database., Setting: Multi-institutional (32 CI centers)., Patients: Adult CI recipients (n = 4,046 patients)., Main Outcome Measures: Mean absolute error (MAE) between imputed and observed speech perception scores., Results: Imputation models of preoperative speech perception measures demonstrate a MAE of less than 10% for feature triplets of CNCw/AzBio in quiet/AzBio +10 (MICE: MAE, 9.52%; 95% confidence interval [CI], 9.40-9.64; KNN: MAE, 8.93%; 95% CI, 8.83-9.03) and AzBio in quiet/AzBio +5/AzBio +10 (MICE: MAE, 8.85%; 95% CI, 8.68-9.02; KNN: MAE, 8.95%; 95% CI, 8.74-9.16) with one feature missing. Postoperative imputation can be safely performed with up to four of six features missing in a set of CNCw and AzBio in quiet at 3, 6, and 12 months postcochlear implantation using MICE (MAE, 9.69%; 95% CI, 9.63-9.76). For multivariable analysis of CI performance prediction, imputation increased sample size by 72%, from 2,756 to 4,739, with marginal change in adjusted R2 (0.13 raw, 0.14 imputed)., Conclusions: Missing data across certain sets of common speech perception tests may be safely imputed, enabling multivariate analysis of one of the largest CI outcomes data sets to date., Competing Interests: Conflicts of Interest: C.P. and D.S.L. do not have conflicts of interest. J.A.H. is a consultant for Cochlear Ltd. C.A.B. is a consultant for Advanced Bionics, Cochlear Ltd., Envoy, and IotaMotion, and has equity interest in Advanced Cochlear Diagnostics, LLC. M.A.S. is a consultant for Cochlear Ltd., (Copyright © 2023, Otology & Neurotology, Inc.)

Objective: Assess the real-world performance of popular imputation algorithms on cochlear implant (CI) candidate audiometric data., Methods: 7,451 audiograms from patients undergoing CI candidacy evaluation were pooled from 32 institutions with complete case analysis yielding 1,304 audiograms. Imputation model performance was assessed with nested cross-validation on randomly generated sparse datasets with various amounts of missing data, distributions of sparsity, and dataset sizes. A threshold for safe imputation was defined as root mean square error (RMSE) <10dB. Models included univariate imputation, interpolation, multiple imputation by chained equations (MICE), k-nearest neighbors, gradient boosted trees, and neural networks., Results: Greater quantities of missing data were associated with worse performance. Sparsity in audiometric data is not uniformly distributed, as inter-octave frequencies are less commonly tested. With 3-8 missing features per instance, a real-world sparsity distribution was associated with significantly better performance compared to other sparsity distributions (Δ RMSE 0.3 dB- 5.8 dB, non-overlapping 99% confidence intervals). With a real-world sparsity distribution, models were able to safely impute up to 6 missing datapoints in an 11-frequency audiogram. MICE consistently outperformed other models across all metrics and sparsity distributions (p < 0.01, Wilcoxon rank sum test). With sparsity capped at 6 missing features per audiogram but otherwise equivalent to the raw dataset, MICE imputed with RMSE of 7.83 dB [95% CI 7.81-7.86]. Imputing up to 6 missing features captures 99.3% of the audiograms in our dataset, allowing for a 5.7-fold increase in dataset size (1,304 to 7,399 audiograms) as compared with complete case analysis., Conclusion: Precision medicine will inevitably play an integral role in the future of hearing healthcare. These methods are data dependent, and rigorously validated imputation models are a key tool for maximizing datasets. Using the largest CI audiogram dataset to-date, we demonstrate that in a real-world scenario MICE can safely impute missing data for the vast majority (>99%) of audiograms with RMSE well below a clinically significant threshold of 10dB. Evaluation across a range of dataset sizes and sparsity distributions suggests a high degree of generalizability to future applications., Competing Interests: AH – Consultant for Cochlear Ltd., CAB – consultant for Advanced Bionics, Cochlear Ltd., Envoy, and IotaMotion, and has equity interest in Advanced Cochlear Diagnostics, LLC.; MAS – consultant for Cochlear Ltd. There are no patents, products in development or marketed products to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2023 Pavelchek et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

A principal challenge in the analysis of tissue imaging data is cell segmentation-the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm. We demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data and models are released as a community resource., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)