Artificial Intelligence-Enhanced Neurocritical Care for Traumatic Brain Injury : Past, Present and Future.

In neurointensive care units (NICU), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of pre-admission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.

Implicit screening of abdominal aortic aneurysms among veterans using lumbar spine MRI.

BACKGROUND: Early detection of abdominal aortic aneurysms (AAAs) is critical given the high morbidity and mortality of a ruptured aneurysm. Screening ultrasound is recommended for men 65 and 75 years of age with a smoking history. However, studies have shown that the rate of ultrasound screening is low and that implicit AAA screening by abdominal imaging studies that were not originally intended for AAA screening can play a major role in AAA detection. OBJECTIVE: The main objective was to evaluate the role of lumbar spine MRIs as an implicit AAA screening study by assessing the detection rate of AAAs in a broader cohort of veterans that included screening and non-screening populations. METHODS: 4085 consecutive lumbar spine MRIs from our institution between 2/2020 and 9/2023 were retrospectively reviewed. Each study was labeled AAA present, AAA not present, or indeterminate by radiologists. The correlation between the presence of AAAs and cardiovascular risk factors was assessed using multinomial logistic regression. RESULTS: AAAs were present in 89 studies (2.2 %) from 80 patients (mean age 75.8 (56-93), M:F 10:0) and absent in 3935 cases (96.3 %) from 3310 patients (mean age 61.7 (19-100), M:F 9:1). Indeterminate cases (n = 61, 1.5 %) were mainly due to incomplete visualization (70.5 %). Mean AAA size was 3.6 cm with most AAAs (n = 43) smaller than 3.5 cm. Sixteen AAAs were 3.5-3.9 cm, 16 between 4 and 4.9cm, and 6 between 5 and 5.9 cm. Artifact precluded measurements in 8 cases. Among the AAA-positive cases, 20 had no prior documentation of AAA. Twenty-one patients with AAAs would not have met the criteria for the routine AAA screening ultrasound. Higher rates of hypertension, hyperlipidemia, and smoking were observed for the AAA cohort at 78.8 % (OR 2.037, CI 1.160-3.576, P = .013), 82.5 % (2.808, 1.543-5.110, P < .001), and 75 % (3.340, 1.979-5.638, P < .001), respectively, compared to the matched no-AAA cohort (58.2 %, 57.6 %, and 50.8 %; n = 2055). CONCLUSION: Lumbar spine MRI is a valid modality for implicit screening of AAAs. CLINICAL IMPACT: Those interpreting lumbar spine MRIs should be vigilant about assessing for AAAs, especially in men with a history of hypertension, hyperlipidemia, or tobacco smoking.

Correlation of Intraoperative Ultrasonographic Oral Tongue Shape and Border and Risk of Close Margins.

Intraoperative ultrasound (IOUS) is a potentially useful adjunct to the resection of oral tongue cancers (OTC). IOUS images of the tumor-normal tissue interface show different patterns of invasion. In this retrospective series of 29 patients treated for OTC, we evaluated if there was a correlation between IOUS findings of patterns of invasion and final histology and assessed if there was any associated risk of increased incidence of positive or close margins with different patterns of invasion as seen on ultrasound. Although we found no significant correlation between ultrasound patterns of invasion and histological evaluation, we did find that an infiltrative pattern of invasion on IOUS did result in a significant risk of a close margin. Further exploration of these findings in a larger prospective study could provide definitive information on the efficacy of this modality in OTC resections.

Incorporating algorithmic uncertainty into a clinical machine deep learning algorithm for urgent head CTs.

Machine learning (ML) algorithms to detect critical findings on head CTs may expedite patient management. Most ML algorithms for diagnostic imaging analysis utilize dichotomous classifications to determine whether a specific abnormality is present. However, imaging findings may be indeterminate, and algorithmic inferences may have substantial uncertainty. We incorporated awareness of uncertainty into an ML algorithm that detects intracranial hemorrhage or other urgent intracranial abnormalities and evaluated prospectively identified, 1000 consecutive noncontrast head CTs assigned to Emergency Department Neuroradiology for interpretation. The algorithm classified the scans into high (IC+) and low (IC-) probabilities for intracranial hemorrhage or other urgent abnormalities. All other cases were designated as No Prediction (NP) by the algorithm. The positive predictive value for IC+ cases (N = 103) was 0.91 (CI: 0.84-0.96), and the negative predictive value for IC- cases (N = 729) was 0.94 (0.91-0.96). Admission, neurosurgical intervention, and 30-day mortality rates for IC+ was 75% (63-84), 35% (24-47), and 10% (4-20), compared to 43% (40-47), 4% (3-6), and 3% (2-5) for IC-. There were 168 NP cases, of which 32% had intracranial hemorrhage or other urgent abnormalities, 31% had artifacts and postoperative changes, and 29% had no abnormalities. An ML algorithm incorporating uncertainty classified most head CTs into clinically relevant groups with high predictive values and may help accelerate the management of patients with intracranial hemorrhage or other urgent intracranial abnormalities.

Fixel-based analysis of the diffusion properties of the patients with brain injury and chronic health symptoms.

The diffusion properties from diffusion tensor imaging (DTI) are sensitive to white matter (WM) abnormalities and could serve as indicators of diffuse axonal damages incurred during a traumatic brain injury (TBI). Analyses of diffusion metrics in the regions of interest (ROIs) were used to compare the differences in the 18 major fiber tracts in 46 participants, between TBI participants with (n = 17) or without (n = 16) chronic symptoms (CS) and a control group (CG, n = 13). In addition to the widely used diffusion metrics, such as fractional anisotropy (FA), mean (MD), axial (AD) and radial (RD) diffusivities, apparent fiber density (AFD), complexity (CX) and fixel number (FN) derived from Mrtrix3 software package were used to characterize WM tracts and compare between participant groups in the ROIs defined by the fixel numbers. Significant differences were found in FA, AFD, MD, RD and CX in ROIs with different FNs in the corpus callosum forceps minor, left and right inferior longitudinal fasciculus, and left and right uncinate fasciculus for both TBI groups compared to controls. Diffusion properties in ROIs with different FNs can serve as detailed biomarkers of WM abnormalities, especially for individuals with chronic TBI related symptoms.

Intracranial Densitometry-Augmented Machine Learning Enhances the Prognostic Value of Brain CT in Pediatric Patients With Traumatic Brain Injury: A Retrospective Pilot Study.

Background: The inter- and intrarater variability of conventional computed tomography (CT) classification systems for evaluating the extent of ischemic-edematous insult following traumatic brain injury (TBI) may hinder the robustness of TBI prognostic models. Objective: This study aimed to employ fully automated quantitative densitometric CT parameters and a cutting-edge machine learning algorithm to construct a robust prognostic model for pediatric TBI. Methods: Fifty-eight pediatric patients with TBI who underwent brain CT were retrospectively analyzed. Intracranial densitometric information was derived from the supratentorial region as a distribution representing the proportion of Hounsfield units. Furthermore, a machine learning-based prognostic model based on gradient boosting (i.e., CatBoost) was constructed with leave-one-out cross-validation. At discharge, the outcome was assessed dichotomously with the Glasgow Outcome Scale (favorability: 1-3 vs. 4-5). In-hospital mortality, length of stay (>1 week), and need for surgery were further evaluated as alternative TBI outcome measures. Results: Densitometric parameters indicating reduced brain density due to subtle global ischemic changes were significantly different among the TBI outcome groups, except for need for surgery. The skewed intracranial densitometry of the unfavorable outcome became more distinguishable in the follow-up CT within 48 h. The prognostic model augmented by intracranial densitometric information achieved adequate AUCs for various outcome measures [favorability = 0.83 (95% CI: 0.72-0.94), in-hospital mortality = 0.91 (95% CI: 0.82-1.00), length of stay = 0.83 (95% CI: 0.72-0.94), and need for surgery = 0.71 (95% CI: 0.56-0.86)], and this model showed enhanced performance compared to the conventional CRASH-CT model. Conclusion: Densitometric parameters indicative of global ischemic changes during the acute phase of TBI are predictive of a worse outcome in pediatric patients. The robustness and predictive capacity of conventional TBI prognostic models might be significantly enhanced by incorporating densitometric parameters and machine learning techniques.

The utility of intra-oral ultrasound in improving deep margin clearance of oral tongue cancer resections.

OBJECTIVES: To investigate the potential utility of intra-oral ultrasound (IOUS) in guiding deep margin clearance and measuring depth of invasion (DOI) of oral tongue carcinomas (OTC). MATERIALS AND METHODS: Retrospective chart review of consecutive patients with T1-T3 OTC who underwent intraoperative ultrasound-guided resection and a comparator group that had undergone resection without the use of IOUS both by a single surgeon. Data was extracted from operative, pathology and radiology reports. Deep margins and DOI were reviewed by a dedicated head and neck pathologist. Correlation between histologic and ultrasound DOI was assessed using Pearson correlation. RESULTS: A total of 23 patients were included in the study cohort with a comparator group of 21 patients in the control group. None of the patients in the study cohort had a positive (cut-through) deep margin and the mean deep margin clearance was 8.5 ± 4.9 and 6.7 ± 3.8 for the IOUS and non-IOUS groups respectively (p-value 0.18) showing a non-significant improvement in the IOUS group. As a secondary outcome, there was a strong correlation between histologic and ultrasound DOI (0.9449). CONCLUSION: Ultrasound appears to be a potentially effective tool in guiding OTC resections. In this small series, IOUS facilitated deep margin clearance and resulted in a non-statistically significant increase in deep margin clearance. Intraoral ultrasound can accurately measure lesional DOI.

Comparison between computed tomography and ultrasound for presurgical evaluation of oral tongue squamous cell carcinoma tumor thickness.

PURPOSE: To compare the accuracy of oral tongue squamous cell carcinoma (OTSCC) tumor thickness (TT) measured on CT to intraoperative ultrasound (US) and histopathology. METHODS AND MATERIALS: Twenty-six patients with OTSCC who underwent tumor resection by a single surgeon with simultaneous intraoperative US between 3/2016 and 4/2019 were prospectively identified, and their data reviewed. TT was independently measured in 19 patients who underwent preoperative CT (cTT) by two neuroradiologists blinded to US and histological results. The confidence level of interpretation of cTT was recorded by each reader using a 5-point Likert scale. The degree of dental artifact on CT was also scored. cTT was compared to TT measured on intraoperative US (uTT) and histopathologic assessment of TT (hTT). RESULTS: OTSCC was visualized on CT in 52% (10/19) and 63% (12/19) of cases for readers 1 and 2, respectively. Mean Likert score was 0.42 for reader 1 and 0.73 for reader 2. Mean cTT of OTSCCs was 5.8 mm +/- 1.7 mm (n = 11). In comparison, mean uTT and hTT were 7.6 mm±3.5 mm and 7.1 +/- 4.2 mm, respectively. The Pearson coefficient (95% confidence interval) was 0.10 (-0.53-0.66) between cTT and hTT (n = 11) and 0.93 (0.74-0.98) between uTT and hTT. CONCLUSIONS: Preoperative CT is not reliable for assessment of TT in OTSCC compared to US and histopathology, particularly for OTSCC under 10 mm. US offers a practical complementary imaging tool with a unique role for primary tumor assessment that can aid in pre-operative planning, especially for small tumors.

Changes in the gray and white matter of patients with ischemic-edematous insults after traumatic brain injury.

OBJECTIVE: Gray matter (GM) and white matter (WM) are vulnerable to ischemic-edematous insults after traumatic brain injury (TBI). The extent of secondary insult after brain injury is quantifiable using quantitative CT analysis. One conventional quantitative CT measure, the gray-white matter ratio (GWR), and a more recently proposed densitometric analysis are used to assess the extent of these insults. However, the prognostic capacity of the GWR in patients with TBI has not yet been validated. This study aims to test the prognostic value of the GWR and evaluate the alternative parameters derived from the densitometric analysis acquired during the acute phase of TBI. In addition, the prognostic ability of the conventional TBI prognostic models (i.e., IMPACT [International Mission for Prognosis and Analysis of Clinical Trials in TBI] and CRASH [Corticosteroid Randomisation After Significant Head Injury] models) were compared to that of the quantitative CT measures. METHODS: Three hundred patients with TBI of varying ages (92 pediatric, 94 adult, and 114 geriatric patients) and admitted between 2008 and 2013 were included in this retrospective cohort study. The normality of the density of the deep GM and whole WM was evaluated as the proportion of CT pixels with Hounsfield unit values of 31-35 for GM and 26-30 for WM on CT images of the entire supratentorial brain. The outcome was evaluated using the Glasgow Outcome Scale (GOS) at discharge (GOS score ≤ 3, n = 100). RESULTS: Lower proportions of normal densities in the deep GM and whole WM indicated worse outcomes. The proportion of normal WM exhibited a significant prognostic capacity (area under the curve [AUC] = 0.844). The association between the outcome and the normality of the WM density was significant in adult (AUC = 0.792), pediatric (AUC = 0.814), and geriatric (AUC = 0.885) patients. In pediatric patients, the normality of the overall density and the density of the GM were indicative of the outcome (AUC = 0.751). The average GWR was not associated with the outcome (AUC = 0.511). IMPACT and CRASH models showed adequate and reliable performance in the pediatric and geriatric groups but not in the adult group. The highest overall predictive performance was achieved by the densitometry-augmented IMPACT model (AUC = 0.881). CONCLUSIONS: Both deep GM and WM are susceptible to ischemic-edematous insults during the early phase of TBI. The extent of the secondary injury was better evaluated by analyzing the normality of the deep GM and WM rather than by calculating the GWR.

Abilities of a Densitometric Analysis of Computed Tomography Images and Hemorrhagic Parameters to Predict Outcome Favorability in Patients With Intracerebral Hemorrhage.

BACKGROUND: Intracerebral hemorrhage (ICH) is one of the most devastating subtypes of stroke. A rapid assessment of ICH severity involves the use of computed tomography (CT) and derivation of the hemorrhage volume, which is often estimated using the ABC/2 method. However, these estimates are highly inaccurate and may not be feasible for anticipating outcome favorability. OBJECTIVE: To predict patient outcomes via a quantitative, densitometric analysis of CT images, and to compare the predictive power of these densitometric parameters with the conventional ABC/2 volumetric parameter and segmented hemorrhage volumes. METHODS: Noncontrast CT images of 87 adult patients with ICH (favorable outcomes = 69, unfavorable outcomes = 12, and deceased = 6) were analyzed. In-house software was used to calculate the segmented hemorrhage volumes, ABC/2 and densitometric parameters, including the skewness and kurtosis of the density distribution, interquartile ranges, and proportions of specific pixels in sets of CT images. Nonparametric statistical analyses were conducted. RESULTS: The densitometric parameter interquartile range exhibited greatest accuracy (82.7%) in predicting favorable outcomes. The combination of skewness and the interquartile range effectively predicted mortality (accuracy = 83.3%). The actual volume of the ICH exhibited good coherence with ABC/2 (R = 0.79). Both parameters predicted mortality with moderate accuracy (<78%) but were less effective in predicting unfavorable outcomes. CONCLUSION: Hemorrhage volume was rapidly estimated and effectively predicted mortality in patients with ICH; however, this value may not be useful for predicting favorable outcomes. The densitometric analysis exhibited significantly higher power in predicting mortality and favorable outcomes in patients with ICH.

Finite element analysis of periventricular lucency in hydrocephalus: extravasation or transependymal CSF absorption?

OBJECTIVE: Periventricular lucency (PVL) is often observed in the hydrocephalic brain on CT or MRI. Earlier studies have proposed the extravasation of ventricular CSF into the periventricular white matter or transependymal CSF absorption as possible causes of PVL in hydrocephalus. However, there is insufficient evidence for either theory to be conclusive. METHODS: A finite element (FE) model of the hydrocephalic brain with detailed anatomical geometry was constructed to investigate the possible mechanism of PVL in hydrocephalus. The initiation of hydrocephalus was modeled by applying a transmantle pressure gradient (TPG). The model was exposed to varying TPGs to investigate the effects of different geometrical characteristics on the distribution of PVL. The edema map was derived based on the interstitial pore pressure. RESULTS: The model simulated the main radiological features of hydrocephalus, i.e., ventriculomegaly and PVL. The degree of PVL, assessed by the pore pressure, was prominent in mild to moderate ventriculomegaly. As the degree of ventriculomegaly exceeded certain values, the pore pressure across the cerebrum became positive, thus inducing the disappearance of PVL. CONCLUSIONS: The results are in accordance with common clinical findings of PVL. The degree of ventriculomegaly significantly influences the development of PVL, but two factors were not linearly correlated. The results are indicative of the transependymal CSF absorption as a possible cause of PVL, but the extravasation theory cannot be formally rejected.

Finite element analysis for normal pressure hydrocephalus: The effects of the integration of sulci.

Finite element analysis (FEA) is increasingly used to investigate the brain under various pathological changes. Although FEA has been used to study hydrocephalus for decades, previous studies have primarily focused on ventriculomegaly. The present study aimed to investigate the pathologic changes regarding sulcal deformation in normal pressure hydrocephalus (NPH). Two finite element (FE) models-an anatomical brain geometric (ABG) model and the conventional simplified brain geometric (SBG) model-of NPH were constructed. The models were constructed with identical boundary conditions but with different geometries. The ABG model contained details of the sulci geometry, whereas these details were omitted from the SBG model. The resulting pathologic changes were assessed via four biomechanical parameters: pore pressure, von Mises stress, pressure, and void ratio. NPH was induced by increasing the transmantle pressure gradient (TPG) from 0 to a maximum of 2.0 mmHg. Both models successfully simulated the major features of NPH (i.e., ventriculomegaly and periventricular lucency). The changes in the biomechanical parameters with increasing TPG were similar between the models. However, the SBG model underestimated the degree of stress across the cerebral mantle by 150% compared with the ABG model. The SBG model also overestimates the degree of ventriculomegaly (increases of 194.5% and 154.1% at TPG = 2.0 mmHg for the SBG and ABG models, respectively). Including the sulci geometry in a FEA for NPH clearly affects the overall results. The conventional SBG model is inferior to the ABG model, which accurately simulated sulcal deformation and the consequent effects on cortical or subcortical structures. The inclusion of sulci in future FEA for the brain is strongly advised, especially for models used to investigate space-occupying lesions.

Thresholds of resistance to CSF outflow in predicting shunt responsiveness.

OBJECTIVES: Disturbances in cerebrospinal fluid (CSF) absorption capacity represent one of the primary causes of normal pressure hydrocephalus (NPH). The resistance to CSF outflow (Rout) may reflect the severity of this disturbance and has been used as a supplementary parameter to predict shunt responsiveness. However, there is no widely accepted threshold of Rout in this scenario. This study aimed to determine the most appropriate threshold through meta-analyses. METHODS: A total of four previously reported cutoff values of Rout (10, 12, 15, and 18 mmHg/ml/minutes) were selected as potential thresholds. A systematic review of existing studies involving NPH, shunt surgeries, and Rout was conducted. The pooled results from retrieved articles were subjected to statistical analyses. RESULTS: A total of nine studies were included in the meta-analyses based on the available preoperative Rout and postoperative improvement of subjects. The odds ratios of the four thresholds were 2·82, 3·58, 2·72, and 2·38 for 10, 12, 15, and 18 mmHg/ml/minutes of Rout, respectively. The Rout of 12 mmHg/ml/minutes yielded high accuracy (72·95%), high sensitivity (80·26%), and moderate specificity (46·79%). DISCUSSION: The presented study conducted meta-analyses and revealed that a Rout of 12 mmHg/ml/minutes is the most suitable threshold for predicting shunt responsiveness in NPH patients.

National trends in burn and inhalation injury in burn patients: results of analysis of the nationwide inpatient sample database.

The aim of this study was describe national trends in prevalence, demographics, hospital length of stay (LOS), hospital charges, and mortality for burn patients with and without inhalational injury and to compare to the National Burn Repository. Burns and inhalation injury cause considerable mortality and morbidity in the United States. There remains insufficient reporting of the demographics and outcomes surrounding such injuries. The National Inpatient Sample database, the nation's largest all-payer inpatient care data repository, was utilized to select 506,628 admissions for burns from 1988 to 2008 based on ICD-9-CM recording. The data were stratified based on the extent of injury (%TBSA) and presence or absence of inhalational injury. Inhalation injury was observed in only 2.2% of burns with <20% TBSA but 14% of burns with 80 to 99% TBSA. Burn patients with inhalation injury were more likely to expire in-hospital compared to those without (odds ratio, 3.6; 95% confidence interval, 2.7-5.0; P < .001). Other factors associated with higher mortality were African-American race, female sex, and urban practice setting. Patients treated at rural facilities and patients with hyperglycemia had lower mortality rates. Each increase in percent of TBSA of burns increased LOS by 2.5%. Patients with burns covering 50 to 59% of TBSA had the longest hospital stay at a median of 24 days (range, 17-55). The median in-hospital charge for a burn patient with inhalation injury was US$32,070, compared to US$17,600 for those without. Overall, patients who expired from burn injury accrued higher in-hospital charges (median, US$50,690 vs US$17,510). Geographically, California and New Jersey were the states with the highest charges, whereas Vermont and Maryland were states with the lowest charges. The study analysis provides a broad sampling of nationwide demographics, LOS, and in-hospital charges for patients with burns and inhalation injury.

Quantitative analysis of computed tomography images and early detection of cerebral edema for pediatric traumatic brain injury patients: retrospective study.

BACKGROUND: The purpose of this study was to identify whether the distribution of Hounsfield Unit (HU) values across the intracranial area in computed tomography (CT) images can be used as an effective diagnostic tool for determining the severity of cerebral edema in pediatric traumatic brain injury (TBI) patients. METHODS: CT images, medical records and radiology reports on 70 pediatric patients were collected. Based on radiology reports and the Marshall classification, the patients were grouped as mild edema patients (n=37) or severe edema patients (n=33). Automated quantitative analysis using unenhanced CT images was applied to eliminate artifacts and identify the difference in HU value distribution across the intracranial area between these groups. RESULTS: The proportion of pixels with HU=17 to 24 was highly correlated with the existence of severe cerebral edema (P<0.01). This proportion was also able to differentiate patients who developed delayed cerebral edema from mild TBI patients. A significant difference between deceased patients and surviving patients in terms of the HU distribution came from the proportion of pixels with HU=19 to HU=23 (P<0.01). CONCLUSIONS: The proportion of pixels with an HU value of 17 to 24 in the entire cerebral area of a non-enhanced CT image can be an effective basis for evaluating the severity of cerebral edema. Based on this result, we propose a novel approach for the early detection of severe cerebral edema.

Axonal mRNA localization and local protein synthesis in nervous system assembly, maintenance and repair.

mRNAs can be targeted to specific neuronal subcellular domains, which enables rapid changes in the local proteome through local translation. This mRNA-based mechanism links extrinsic signals to spatially restricted cellular responses and can mediate stimulus-driven adaptive responses such as dendritic plasticity. Local mRNA translation also occurs in growing axons where it can mediate directional responses to guidance signals. Recent profiling studies have revealed that both growing and mature axons possess surprisingly complex and dynamic transcriptomes, thereby suggesting that axonal mRNA localization is highly regulated and has a role in a broad range of processes, a view that is increasingly being supported by new experimental evidence. Here, we review current knowledge on the roles and regulatory mechanisms of axonal mRNA translation and discuss emerging links to axon guidance, survival, regeneration and neurological disorders.

Local translation of extranuclear lamin B promotes axon maintenance.

Local protein synthesis plays a key role in regulating stimulus-induced responses in dendrites and axons. Recent genome-wide studies have revealed that thousands of different transcripts reside in these distal neuronal compartments, but identifying those with functionally significant roles presents a challenge. We performed an unbiased screen to look for stimulus-induced, protein synthesis-dependent changes in the proteome of Xenopus retinal ganglion cell (RGC) axons. The intermediate filament protein lamin B2 (LB2), normally associated with the nuclear membrane, was identified as an unexpected major target. Axonal ribosome immunoprecipitation confirmed translation of lb2 mRNA in vivo. Inhibition of lb2 mRNA translation in axons in vivo does not affect guidance but causes axonal degeneration. Axonal LB2 associates with mitochondria, and LB2-deficient axons exhibit mitochondrial dysfunction and defects in axonal transport. Our results thus suggest that axonally synthesized lamin B plays a crucial role in axon maintenance by promoting mitochondrial function.