The influence of femoral lytic tumors segmentation on autonomous finite element analysis.

BACKGROUND: The validated CT-based autonomous finite element system Simfini (Yosibash et al., 2020) is used in clinical practice to assist orthopedic oncologists in determining the risk of pathological femoral fractures due to metastatic tumors. The finite element models are created automatically from CT-scans, assigning to lytic tumors a relatively low stiffness as if these were a low-density bone tissue because the tumors could not be automatically identified. METHODS: The newly developed automatic deep learning algorithm which segments lytic tumors in femurs, presented in (Rachmil et al., 2023), was integrated into Simfini. Finite element models of twenty femurs from ten CT-scans of patients with femoral lytic tumors were analyzed three times using: the original methodology without tumor segmentation, manual segmentation of the lytic tumors, and the new automatic segmentation deep learning algorithm to identify lytic tumors. The influence of explicitly incorporating tumors in the autonomous finite element analysis on computed principal strains is quantified. These serve as an indicator of femoral fracture and are therefore of clinical significance. FINDINGS: Autonomous finite element models with segmented lytic tumors had generally larger strains in regions affected by the tumor. The deep learning and manual segmentation of tumors resulted in similar average principal strains in 19 regions out of the 23 regions within 15 femurs with lytic tumors. A high dice similarity score of the automatic deep learning tumor segmentation did not necessarily correspond to minor differences compared to manual segmentation. INTERPRETATION: Automatic tumor segmentation by deep learning allows their incorporation into an autonomous finite element system, resulting generally in elevated averaged principal strains that may better predict pathological femoral fractures.

Hip Fracture Risk Assessment in Elderly and Diabetic Patients: Combining Autonomous Finite Element Analysis and Machine Learning.

Autonomous finite element analyses (AFE) based on CT scans predict the biomechanical response of femurs during stance and sidewise fall positions. We combine AFE with patient data via a machine learning (ML) algorithm to predict the risk of hip fracture. An opportunistic retrospective clinical study of CT scans is presented, aimed at developing a ML algorithm with AFE for hip fracture risk assessment in type 2 diabetic mellitus (T2DM) and non-T2DM patients. Abdominal/pelvis CT scans of patients who experienced a hip fracture within 2 years after an index CT scan were retrieved from a tertiary medical center database. A control group of patients without a known hip fracture for at least 5 years after an index CT scan was retrieved. Scans belonging to patients with/without T2DM were identified from coded diagnoses. All femurs underwent an AFE under three physiological loads. AFE results, patient's age, weight, and height were input to the ML algorithm (support vector machine [SVM]), trained by 80% of the known fracture outcomes, with cross-validation, and verified by the other 20%. In total, 45% of available abdominal/pelvic CT scans were appropriate for AFE (at least 1/4 of the proximal femur was visible in the scan). The AFE success rate in automatically analyzing CT scans was 91%: 836 femurs we successfully analyzed, and the results were processed by the SVM algorithm. A total of 282 T2DM femurs (118 intact and 164 fractured) and 554 non-T2DM (314 intact and 240 fractured) were identified. Among T2DM patients, the outcome was: Sensitivity 92%, Specificity 88% (cross-validation area under the curve [AUC] 0.92) and for the non-T2DM patients: Sensitivity 83%, Specificity 84% (cross-validation AUC 0.84). Combining AFE data with a ML algorithm provides an unprecedented prediction accuracy for the risk of hip fracture in T2DM and non-T2DM populations. The fully autonomous algorithm can be applied as an opportunistic process for hip fracture risk assessment. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Frequency of Alzheimer's disease pathology at autopsy in patients with clinical normal pressure hydrocephalus.

BACKGROUND: Normal pressure hydrocephalus (NPH) is considered to be potentially treatable with the placement of a cerebrospinal fluid (CSF) shunt. However, the procedure has been reported to have variable success, particularly with respect to improving the cognitive impairment in NPH. The presence of neurologic comorbidities, particularly Alzheimer's disease (AD), may contribute to shunt responsiveness. Uncovering the extent to which AD and NPH co-occur has implications for diagnosis and treatment of NPH. Autopsy studies of patients with NPH during their lifetime would elucidate the frequency of such comorbidities. METHODS: A search of the Sun Health Research Institute Brain Donation Program database was conducted between January 1, 1997 and April 1, 2009 to identify all cases with neuropathologic evidence of dementia as well as those of clinically diagnosed NPH. We reviewed the medical records and brain findings of each NPH case. RESULTS: Of the 761 cases autopsied over the study interval, 563 were found to have neuropathologic evidence meeting criteria for a dementing illness. Of 563 cases, AD was found exclusively in 313 (56%), and 94 suffered from secondary diagnosis of dementia. Nine of 761 cases were identified with a clinical diagnosis of NPH, which were among the 563 cases with neuropathology of dementing illness at autopsy, representing 1.6% (9/563) of the cases. On review of brain autopsy reports of these nine patients, eight (89%) were found to have AD and one (11%) had progressive supranuclear palsy. Review of the medical records of the nine NPH cases revealed the following clinical comorbidities: five suffered from AD, one from Parkinson's Disease, one from mild cognitive impairment, and one from seizure disorder. CONCLUSIONS: Given the findings of the present study, we support the AD-NPH theory and posit that AD is a common pathologic comorbidity in the setting of NPH and may preclude cognitive improvement postshunt placement. This may influence the selection of cases for shunting in the future.

Probable early-onset Alzheimer's disease in an apolipoprotein E2 homozygote.

OBJECTIVE: To describe a case of early-onset Alzheimer's disease (AD) in an apolipoprotein (Apo) ε2/ε2 homozygote. BACKGROUND: Apo ε2/ε2 is the rarest of the ApoE genotypes, representing only 1.4% of the population. Cognitive decline in ApoE ε2 homozygotes has rarely been reported. CASE REPORT/METHODS: We report a 58-year-old Apo ε2/ε2 female who meets clinical criteria for probable AD as confirmed by neuropsychological testing, positron emission/computed tomography scan, CSF analysis and genetic screening for known mutations. RESULTS: The clinical course is typical of AD, with progressive cognitive and functional decline. CONCLUSION: Clinically confirmed early-onset AD is atypical in ApoE2 homozygotes but can occur.