Patient Self-Scanning for Lung Ultrasound: A Prospective Observational Study on the Feasibility and Diagnostic Accuracy of a Telemedicine Protocol
Bibliographic record
Abstract
OBJECTIVE: To evaluate the feasibility and diagnostic accuracy of patient-performed lung ultrasound (P-PLUS) for telemedicine purposes. METHODS: This prospective observation study included patients over 18 years old who presented to a tertiary care hospital's emergency department. Patients were provided a five-minute instructional video on a US protocol of four lung zones and then performed the protocol while being monitored by a study investigator. The physician sonographer subsequently repeated the protocol. Two emergency physicians with US fellowship training blindly reviewed and independently rated image quality on a scale of one to five, with a score of three or more considered interpretable. Inter-rater reliability was estimated using the intraclass correlation coefficient. Wilcoxon-Mann-Whitney tests and chi-square tests were used to compare group differences. RESULTS: A total of 56 patients (45% female) were enrolled, and 417 clips were analyzed. Ten (18%) participants worked in the medical field, and 44 (79%) had at least some college education. Forty (71%) regularly used technology at work, 52 (93%) had internet access at home, and the same number had access to smartphones. Patients reported high comfort in performing self-LUS (median score: 4, interquartile range (IQR): 3.5-5) and high willingness to perform US acquisition again in the future (median score: 4, IQR: 4-5). The proportion of interpretable images was similar between the two groups except for the left hemidiaphragm (90% of provider-obtained images were interpretable vs. 45% of patient-obtained images, P = 0.002). The majority of patient-obtained images were scored between three and four and classified as interpretable. Mean image scores were significantly higher for provider-obtained images (P < 0.05). Inter-rater reliability between the two raters was good (intraclass correlation coefficient (ICC)= 0.80, 95% CI 0.76-0.84). CONCLUSION: Patients can independently obtain interpretable LUS images in all views with minimal video-based instruction. The ability of patients to obtain interpretable LUS images with minimal tele-guidance, as well as their high levels of comfort and willingness to perform the procedure, support the potential use of P-PLUS in home-based and remote patient care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".