Implementation of artificial intelligence in detection, classification, and prognostication of osteosarcoma utilizing different assessment techniques: a systematic review
Bibliographic record
Abstract
Introduction Osteosarcoma (OS) is the most common primary bone cancer particularly in individuals aged 0-19, classified into different stages. Early diagnosis improves survival, Determination of prognosis and treatment based on it, and enables limb-sparing surgery. AI, in particular machine learning (ML) and deep learning (DL), helps analyze large datasets, identify biomarkers, predict prognosis, and personalize treatments by assessing the aforementioned features. AI has the potential to improve evaluation procedures, such as imaging and pathology approaches used in OS diagnosis, prognosis, and treatment. This study systematically examines AI’s synergistic role with conventional evaluating techniques in OS treatment, improving prognostication, predicting therapy responses, and developing personalized treatment strategies. Method We performed an extensive search via several databases until April 23, 2024. Machine learning (ML), deep learning (DL) as the main branches of AI are often utilized in the medical sciences were searched for detection classification, and prognostication of osteosarcoma. RAYYAN.ai was used to screen the articles through the titles and abstracts. We conducted data extraction on the included articles and employed Cochrane and QUIPS tools to assess potential bias in the included non-prognosis and prognosis studies to evaluate their quality, respectively. Results There were 8129 articles obtained from the four databases following a thorough search. Of them 8050 ones were excluded and the remaining 78 articles published from 2013 to 2024 were reviewed. A large number of the articles indicated moderate and low risk of bias as a result of the risk of bias assessment. The majority of the articles that were reviewed (n = 48) concerned the clinical aspects of osteosarcoma; of these, 23 and 25 studies assessed diagnosis and prognoses, respectively. Furthermore, 20 articles examined image analysis specifically, 4 examined image segmentation methods, and 16 introduced classifiers to identify osteosarcoma from other diseases. Conclusion AI improves biomarker identification, diagnostics, and prognosis of osteosarcoma through medical imaging and data integration. Models like ResNet50 and CNN show high performance but face real-world limitations due to data heterogeneity and overfitting. This study explores AI’s role in osteosarcoma diagnosis, emphasizing interdisciplinary collaboration, external validation, and real-world application challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".