How machine learning is embedded to support clinician decision making: an analysis of FDA-approved medical devices

Objective To examine how and to what extent medical devices using machine learning (ML) support clinician decision making.Methods We searched for medical devices that were (1) approved by the US Food and Drug Administration (FDA) up till February 2020; (2) intended for use by clinicians; (3) in clinical tasks or decisions and (4) used ML. Descriptive information about the clinical task, device task, device input and output, and ML method were extracted. The stage of human information processing automated by ML-based devices and level of autonomy were assessed.Results Of 137 candidates, 59 FDA approvals for 49 unique devices were included. Most approvals (n=51) were since 2018. Devices commonly assisted with diagnostic (n=35) and triage (n=10) tasks. Twenty-three devices were assistive, providing decision support but left clinicians to make important decisions including diagnosis. Twelve automated the provision of information (autonomous information), such as quantification of heart ejection fraction, while 14 automatically provided task decisions like triaging the reading of scans according to suspected findings of stroke (autonomous decisions). Stages of human information processing most automated by devices were information analysis, (n=14) providing information as an input into clinician decision making, and decision selection (n=29), where devices provide a decision.Conclusion Leveraging the benefits of ML algorithms to support clinicians while mitigating risks, requires a solid relationship between clinician and ML-based devices. Such relationships must be carefully designed, considering how algorithms are embedded in devices, the tasks supported, information provided and clinicians’ interactions with them

Three-dimensional magnetic resonance imaging of physeal injury: reliability and clinical utility

BACKGROUND: Injuries to the physis are common in children with a subset resulting in an osseous bar and potential growth disturbance. Magnetic resonance imaging allows for detailed assessment of the physis with the ability to generate 3-dimensional physeal models from volumetric data. The purpose of this study was to assess the interrater reliability of physeal bar area measurements generated using a validated semiautomated segmentation technique and to highlight the clinical utility of quantitative 3-dimensional (3D) physeal mapping in pediatric orthopaedic practice. METHODS: The Radiology Information System/Picture Archiving Communication System (PACS) at our institution was searched to find consecutive patients who were imaged for the purpose of assessing a physeal bar or growth disturbance between December 2006 and October 2011. Physeal segmentation was retrospectively performed by 2 independent operators using semiautomated software to generate physeal maps and bar area measurements from 3-dimensional spoiled gradient recalled echo sequences. Inter-reliability was statistically analyzed. Subsequent surgical management for each patient was recorded from the patient notes and surgical records. RESULTS: We analyzed 24 patients (12M/12F) with a mean age of 11.4 years (range, 5-year to 15-year olds) and 25 physeal bars. Of the physeal bars: 9 (36%) were located in the distal tibia; 8 (32%) in the proximal tibia; 5 (20%) in the distal femur; 1 (4%) in the proximal femur; 1 (4%) in the proximal humerus; and 1 (4%) in the distal radius. The independent operator measurements of physeal bar area were highly correlated with a Pearson correlation coefficient (r) of 0.96 and an intraclass correlation coefficient for average measures of 0.99 (95% confidence interval, 0.97-0.99). Four patients underwent resection of the identified physeal bars, 9 patients were treated with epiphysiodesis, and 1 patient underwent bilateral tibial osteotomies. CONCLUSIONS: Semiautomated segmentation of the physis is a reproducible technique for generating physeal maps and accurately measuring physeal bars, providing quantitative and anatomic information that may inform surgical management and prognosis in patients with physeal injury

Assessment of osteonecrosis in the presence of instrumentation for femoral neck fracture using contrast-enhanced mavric sequence

BACKGROUND Evaluating postoperative femoral neck facture (FNF) with metal fixation hardware is commonly performed using radiographs. MRI has greater sensitivity and specificity to evaluate osteonecrosis (ON) but is often challenging due to the image distortion caused by metallic hardware. QUESTIONS/PURPOSES The aim of this study is to compare fast spin-echo (FSE) and multi-acquisition variable-resonance image combination (MAVRIC) sequences in assessing ON following metallic fixation of FNF and determining feasibility of semi-quantitative perfusion using MAVRIC. METHODS Radiography and MRI were performed at 3 and 12 months postoperatively, using FSE and pre- and post-gadolinium contrast MAVRIC sequences in 21 FNF patients. The presence and volume of ON were recorded. Signal intensity (SI) enhancement was measured on the MAVRIC sequences within the center and rim of ON; with the ilium and femoral diaphysis as controls. The detection rate of ON between MAVRIC and FSE images was evaluated as the difference of percent enhancement across the defined regions of interest. RESULTS ON was detected in 0% of radiographs, in 67% of FSE, and in 76% of MAVRIC images at 3 months follow-up, with similar results at 12 months. MAVRIC images had larger ON volume than FSE images at both time points. A significant percentage SI enhancement was only detected in the ON rim. CONCLUSION Radiographs could not detect ON following metallic fixation of FNF. MAVRIC is more sensitive than FSE for determining the volume of ON. SI measurements using MAVRIC may provide an indirect assessment of perfusion