An Artificial Intelligence-Based Support Tool for Lumbar Spinal Stenosis Diagnosis from Self-Reported History Questionnaire.

OBJECTIVES: Symptomatic lumbar spinal stenosis (LSS) leads to functional impairment and pain. While radiologic characterization of the morphological stenosis grade can aid in the diagnosis, it may not always correlate with patient symptoms. Artificial intelligence (AI) may diagnose symptomatic LSS in patients solely based on self-reported history questionnaires. METHODS: We evaluated multiple machine learning (ML) models to determine the likelihood of LSS using a self-reported questionnaire in patients experiencing low back pain and/or numbness in the legs. The questionnaire was built from peer-reviewed literature and a multidisciplinary panel of experts. Random forest, lasso logistic regression, support vector machine, gradient boosting trees, deep neural networks, and automated machine learning models were trained and performance metrics were compared. RESULTS: Data from 4827 patients (4690 patients without LSS: mean age 62.44, range 27-84 years, 62.8% females, and 137 patients with LSS: mean age 50.59, range 30-71 years, 59.9% females) were retrospectively collected. Among the evaluated models, the random forest model demonstrated the highest predictive accuracy with an area under the receiver operating characteristic curve (AUROC) between model prediction and LSS diagnosis of 0.96, a sensitivity of 0.94, a specificity of 0.88, a balanced accuracy of 0.91, and a Cohen's kappa of 0.85. CONCLUSIONS: Our results indicate that ML can automate the diagnosis of LSS based on self-reported questionnaires with high accuracy. Implementation of standardized and intelligence-automated workflow may serve as a supportive diagnostic tool to streamline patient management and potentially lower health care costs.

Treatment of Substandard Rocket Fuel 1,1-Dimethylhydrazine via Its Methylene Derivative into Heterocycles Based on Pyrrolo-[3,4c]Quinolines, Cyclododeca[b]piran and Pyrrole.

1,1-Dimethylhydrazine (Heptil, rocket fuel (UDMH)) is characterized by extremely high toxicity, teratogenicity and the ability to constantly absorb water from the atmosphere, losing its energy characteristics. In this regard, as well as due to the alternative fuel ("Angara") transition, there is a need for UDMH utilization in huge amounts. A more benign approach involves its immediate reaction with a formalin solution to form 1,1-dimethyl-2-methylene hydrazone (MDH), which is significantly less toxic by an order of magnitude. MDH can then be polymerized under acidic conditions, and the resulting product can be burned, yielding a substantial amount of nitrogen oxides. We propose an alternative to incineration by involving MDH in organic synthesis. We studied the reactions of MDH and its analog N,N-dimethyl-2-(methylenamino)ethane-1-amine (MDEA) with available CH-acids: tetracyanoethylated ketones (TCEKs) based on cyclohexanone, 4-propylcyclohexanone, 2-methylcyclohexanone, cyclododecanone and tetracyanoethane. The structures synthesized were confirmed by IR, 1H, 13C NMR and mass spectroscopy methods. MDH-based adducts were also identified by X-ray structural analysis. TCEKs and MDH, as well as TCEK based on cyclohexanone and MDEA, form bi- and tricyclic structures: pyrrolo [3,4c]-quinolines (using TCEKs based on cyclohexanone and 4-propylcyclohexanone), epiminomethanoquinoline-3,4-dicarbonitrile (using TCEK based on 2-methylcyclohexanone) and cyclododec[b]pyran-3,4-dicarbonitrile (using TCEK based on cyclododecanone). MDH and TCNEH2 formed a pyrrole derivative. Thus, we synthesized the structures that are of interest for molecular design and pharmaceutical chemistry.