Modern Practice for Accurate Prenatal Detection of Cleft Lip & Cleft Palate; Literature Review
Bibliographic record
Abstract
Introduction: Orofacial clefts, including cleft lip and/or palate are prevalent congenital anomalies forming between the 4th and 12th week of gestation which show varying incidences worldwide. Orofacial clefts can be syndromic or non-syndromic with both environmental and genetic factors increasing the risk of developing these structural defects. Management involves a multidisciplinary team to addresses structural, functional, cosmetic and psychological aspects of these defects which require early and accurate diagnosis during gestation. This literature review aims to identify imaging techniques and modalities including 2D, 3D and MRI to accurately diagnose cleft lip and palate. Method: A qualitative narrative form of literature review was carried out using PubMed, ScienceDirect and Cochrane library. Published articles between January 2010 and March 2024 were reviewed. PRISMA flow chart was used to display selection process. Methodology quality of each study was assessed using the Newcastle-Ottawa scale for cohort studies and QUADAS-2 scale for diagnostic studies. Results: 21 studies met the eligibility criteria to be included in this literature review with 12 studies being diagnostic and 9 observational studies. 17 studies utilized 2D ultrasound with 9 studies comparing 2D ultrasound to 3D ultrasound and 7 studies comparing ultrasound with MRI. Some studies proposed novel techniques using 2D Ultrasound. Overall, studies suggested that the combined use of 2D US with 3D or MRI may improve diagnostic accuracy of detecting orofacial cleft prenatally. Conclusion: 2D ultrasound is the initial imaging modality used for imaging during early gestation however high risk pregnancies require referral to tertiary centres for evaluation suing 3D ultrasound and MRI for accurate diagnosis of orofacial clefts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.032 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".