Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning

Purpose: Hip fractures are a common cause of morbidity and mortality. Automatic identification and classification of hip fractures using deep learning may improve outcomes by reducing diagnostic errors and decreasing time to operation. Methods: Hip and pelvic radiographs from 1118 studies were reviewed and 3034 hips were labeled via bounding boxes and classified as normal, displaced femoral neck fracture, nondisplaced femoral neck fracture, intertrochanteric fracture, previous ORIF, or previous arthroplasty. A deep learning-based object detection model was trained to automate the placement of the bounding boxes. A Densely Connected Convolutional Neural Network (DenseNet) was trained on a subset of the bounding box images, and its performance evaluated on a held out test set and by comparison on a 100-image subset to two groups of human observers: fellowship-trained radiologists and orthopaedists, and senior residents in emergency medicine, radiology, and orthopaedics. Results: The binary accuracy for fracture of our model was 93.8% (95% CI, 91.3-95.8%), with sensitivity of 92.7% (95% CI, 88.7-95.6%), and specificity 95.0% (95% CI, 91.5-97.3%). Multiclass classification accuracy was 90.4% (95% CI, 87.4-92.9%). When compared to human observers, our model achieved at least expert-level classification under all conditions. Additionally, when the model was used as an aid, human performance improved, with aided resident performance approximating unaided fellowship-trained expert performance. Conclusions: Our deep learning model identified and classified hip fractures with at least expert-level accuracy, and when used as an aid improved human performance, with aided resident performance approximating that of unaided fellowship-trained attendings.Comment: Presented at Orthopaedic Research Society, Austin, TX, Feb 2, 2019, currently in submission for publicatio

Development of a simulation-based curriculum for Pediatric prehospital skills: a mixed-methods needs assessment.

BackgroundThe assessment and treatment of pediatric patients in the out-of-hospital environment often presents unique difficulties and stress for EMS practitioners.ObjectiveUse a mixed-methods approach to assess the current experience of EMS practitioners caring for critically ill and injured children, and the potential role of a simulation-based curriculum to improve pediatric prehospital skills.MethodsData were obtained from three sources in a single, urban EMS system: a retrospective review of local pediatric EMS encounters over one year; survey data of EMS practitioners' comfort with pediatric skills using a 7-point Likert scale; and qualitative data from focus groups with EMS practitioners assessing their experiences with pediatric patients and their preferred training modalities.Results2.1% of pediatric prehospital encounters were considered "critical," the highest acuity level. A total of 136 of approximately 858 prehospital providers responded to the quantitative survey; 34.4% of all respondents either somewhat disagree (16.4%), disagree (10.2%), or strongly disagree (7.8%) with the statement: "I feel comfortable taking care of a critically ill pediatric patient." Forty-seven providers participated in focus groups that resulted in twelve major themes under three domains. Specific themes included challenges in medication dosing, communication, and airway management. Participants expressed a desire for more repetition and reinforcement of these skills, and they were receptive to the use of high-fidelity simulation as a training modality.ConclusionsCritically ill pediatric prehospital encounters are rare. Over one third of EMS practitioners expressed a low comfort level in managing critically ill children. High-fidelity simulation may be an effective means to improve the comfort and skills of prehospital providers

Experimental pain and opioid analgesia in volunteers at high risk for obstructive sleep apnea.

Obstructive sleep apnea (OSA) is characterized by recurrent nocturnal hypoxia and sleep disruption. Sleep fragmentation caused hyperalgesia in volunteers, while nocturnal hypoxemia enhanced morphine analgesic potency in children with OSA. This evidence directly relates to surgical OSA patients who are at risk for airway compromise due to postoperative use of opioids. Using accepted experimental pain models, we characterized pain processing and opioid analgesia in male volunteers recruited based on their risk for OSA.After approval from the Intitutional Review Board and informed consent, we assessed heat and cold pain thresholds and tolerances in volunteers after overnight polysomnography (PSG). Three pro-inflammatory and 3 hypoxia markers were determined in the serum. Pain tests were performed at baseline, placebo, and two effect site concentrations of remifentanil (1 and 2 µg/ml), an μ-opioid agonist. Linear mixed effects regression models were employed to evaluate the association of 3 PSG descriptors [wake after sleep onset, number of sleep stage shifts, and lowest oxyhemoglobin saturation (SaO(2)) during sleep] and all serum markers with pain thresholds and tolerances at baseline, as well as their changes under remifentanil.Forty-three volunteers (12 normal and 31 with a PSG-based diagnosis of OSA) were included in the analysis. The lower nadir SaO(2) and higher insulin growth factor binding protein-1 (IGFBP-1) were associated with higher analgesic sensitivity to remifentanil (SaO(2), P = 0.0440; IGFBP-1, P = 0.0013). Other pro-inflammatory mediators like interleukin-1β and tumor necrosis factor-α (TNF-α) were associated with an enhanced sensitivity to the opioid analgesic effect (IL-1β, P = 0.0218; TNF-α, P = 0.0276).Nocturnal hypoxemia in subjects at high risk for OSA was associated with an increased potency of opioid analgesia. A serum hypoxia marker (IGFBP-1) was associated with hypoalgesia and increased potency to opioid analgesia; other pro-inflammatory mediators also predicted an enhanced opioid potency.Clinicaltrials.gov NCT00672737

Volunteers and study procedures.

<p>A total of 167 volunteers were screened via telephone or a personal interview. Fifty six volunteers signed an informed concent; 3 of them withdrew before participation in any of the study procedures. All remaining 53 underwent an overnight polysomnography, but only 49 came for the pain test on the second study day. Blood was not drawn from 3 volunteers due to technical difficulties, while 2 more blood samples were lost before analysis was done; a total of 44 blood samples provided data on inflammatory and hypoxia markers. The first 9 volunteers were tested on remifentanil Ce of 2 and 4 µg/ml and all the remaining on 1 and 2 µg/ml. Two volunteers experienced intence nausea; one at the end and the other at the beginning of the first remifentanil Ce (2 µg/ml); the experiment was stopped in both cases, but pain-related measurements were acquired only in the first case. A pump malfunction prohibited the administration of remifentanil in 1 subject. One volunteer was excluded from the analysis due to an inadequate sleep study. Finally, data from 48 volunteers were included in the analysis.</p

Linear regression results for the effect of sleep, inflammation, and hypoxia markers on heat pain at baseline and under remifentanil.

<p>Sleep continuity and breathing polysomnographic (PSG) descriptors like the wake after sleep onset (WASO) time, number of sleep stage shifts, and the lowest recorded SaO₂ during sleep (nadir SaO₂), as well as basic inflammation and hypoxia markers were employed as predictors of heat pain-related variables (threshold and tolerance) in the model. At baseline, betas (standard errors, SE) for the various predictors in the regression equation reflect the change in the heat pain threshold and tolerance for every one unit of change in the PSG and inflammatory markers. Under remifentanil, betas reflect the change in the analgesic sensitivity to remifentanil for the heat pain threshold and tolerance. For example, for every 1-pg/ml-increase in the serum IL-1β the heat pain tolerance will additionally increase by 0.3899°C for every 1-µg/ml-increase in the target Ce of remifentanil. Betas for the PSG predictors were adjusted for the inflammatory and hypoxia markers. Alpha level was set at 0.05.</p

Linear regression results for the effect of sleep, inflammatory, and hypoxia-related markers on cold-induced pain at baseline and under remifentanil.

<p>Sleep continuity and breathing polysomnographic (PSG) descriptors like the wake after sleep onset (WASO) time, number of sleep stage shifts, and the lowest recorded SaO₂ during sleep (nadir SaO₂), as well as basic inflammation and hypoxia markers were employed as predictors of heat pain-related variables (threshold and tolerance) in the model. At baseline, betas (standard errors, SE) for the various predictors in the regression equation reflect the change in the cold pain threshold and tolerance for every one unit of change in the PSG and inflammatory markers. Under remifentanil, betas reflect the change in the analgesic sensitivity to remifentanil for the cold pain threshold and tolerance. For example, for every 1-%-absolute decrease in the nadir SaO₂ the cold pain threshold will additionally increase by 0.9694°C for every 1-µg/ml-increase in the target Ce of remifentanil. Betas for the PSG predictors were adjusted for the inflammatory and hypoxia markers. Alpha level was set at 0.05.</p

Study flow scheme.

<p>On the first study day, the volunteers underwent home-based or in-hospital overnight polysomnography (PSG). Approximately a week later, they underwent experimental pain testing after a blood sample was withdrawn for determination of pro-inflammatory and hypoxia markers. Threshold and tolerances were determined for both heat and cold painful stimuli at baseline (no drug), placebo, and two (low and high) effect site concentrations (Ce) of remifentanil (1 and 2 µg/ml, or 2 and 4 µg/ml) that were targeted using an electronic syringe pump connected to a laptop equiped with a special software driver (STANPUMP, written by Steve L. Shafer, MD) and a pharmacokinetic-pharmacodynamic model for remifentanil. The order of presentation for the two remifentanil Ce was randomized, using special software and sealed envelopes that were opened on the day of trial.</p

Baseline morphometrics and demographics, metabolic, sleepiness, mood, and pro-inflammatory markers.

<p>Age, body mass index (BMI), ethnicity, and baseline fasting morning glucose, glycated hemoglobin (HbA1c), Epworth sleepiness scale (ESS), and Beck depression inventory (BDI) for the 48 volunteers. Inflammation and hypoxia markers were determined in 44 out of the 48 volunteers, but only 43 of them were included in the analysis. All parameters are summarized as medians (range) separately for the volunteers with (apnea/hypopnea index, AHI≥5 events/h, N = 33) and without (AHI<5, N = 15) a PSG-based diagnosis of obstructive sleep apnea (OSA). Two volunteers had a BMI>30 kg/m², each belonging to each AHI class. Comparisons between subjects with and without OSA were performed with an appropriate non-paired test, while distribution of race was assessed by X² test; alpha level was set at 0.05.</p

Evaluating the predictability of medical conditions from social media posts.

We studied whether medical conditions across 21 broad categories were predictable from social media content across approximately 20 million words written by 999 consenting patients. Facebook language significantly improved upon the prediction accuracy of demographic variables for 18 of the 21 disease categories; it was particularly effective at predicting diabetes and mental health conditions including anxiety, depression and psychoses. Social media data are a quantifiable link into the otherwise elusive daily lives of patients, providing an avenue for study and assessment of behavioral and environmental disease risk factors. Analogous to the genome, social media data linked to medical diagnoses can be banked with patients' consent, and an encoding of social media language can be used as markers of disease risk, serve as a screening tool, and elucidate disease epidemiology. In what we believe to be the first report linking electronic medical record data with social media data from consenting patients, we identified that patients' Facebook status updates can predict many health conditions, suggesting opportunities to use social media data to determine disease onset or exacerbation and to conduct social media-based health interventions