Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate—A Systematic Review
Abstract
:1. Introduction
1.1. Rationale and Objectives
1.2. Research Questions
- What are the current clinical applications of deep learning/artificial intelligence in patients with CLP?
- What is the diagnostic performance of AI and ML models being utilized on CLP patients?
2. Materials and Methods
2.1. Research Design
2.2. Eligibility Criteria
2.2.1. Inclusion Criteria
- −
- The articles that dealt with AI and its application in the context of CLP.
- −
- The journal articles which present some predictability or observable outcomes using Machine learning techniques in children with CLP.
- −
- Original articles, Case-control studies, longitudinal observational studies, and retrospective cross-sectional studies that involves artificial intelligent or machine learning neural network methods in children with CLP.
2.2.2. Exclusion Criteria
- −
- Unpublished articles that have been uploaded with only manuscripts.
- −
- Articles that contain only abstracts without their full text.
- −
- Journal articles which were published in languages other than English.
- −
- Book chapters, magazine prints, blog posts, editorials, case reports and case series.
2.3. Information Sources
2.4. Search Strategy
2.5. Study Selection and Data Collection Process
2.6. Data Extraction
2.7. Data Items
- (a)
- Population—Children with Cleft lip and palate of either sex, and of any ethnicity.
- (b)
- Intervention—The applications of AI/ML techniques in diagnosis and treatment prediction in children with CLP.
- (c)
- Comparison—Human intelligence/other diagnostic methods which does not involve AI models.
- (d)
- Outcomes—Diagnostic accuracy and prediction of treatment outcome in children with CLP.
2.8. Diagnostic Accuracy Measures
2.9. Characteristics for Diagnostic Comparisons
- (i)
- Index test: the sensitivity and specificity of clinically trained AI/machine learning models are tested using an index test and evaluating parameters.
- (ii)
- Reference standards: any other assessment techniques such as Mel frequency for hypernasality, lateral cephalometric radiographic evaluation by clinicians.
- (iii)
- Target conditions: Unilateral CLP, Bilateral CLP, Unilateral Cleft lip and alveolus, Unilateral cleft lip, Hypernasality, Dental characteristics and sagittal relationship in children with CLP.
2.10. Risk of Bias Assessment
2.11. Additional Synthesis
3. Results
3.1. Study Selection
3.2. Characteristics of the Included Studies
3.3. Results of Risk of Bias Studies
3.4. Clinical Applications of AI
3.5. Genetic Risk Assessment
3.6. Dental Characteristics and Sagittal Jaw Relationship
3.7. Hypernasality Detection
3.8. CLP Surgery
3.9. Diagnosis and Prediction
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ANB | A-point, nasion, B-point |
ANN | Artificial neural network |
BCLP | Bilateral CLP |
CLP | cleft lip and palate |
CNN | Convolutional neural network |
COP | Cant of occlusal plane |
CSR | Cascaded shaped regression |
DNN | Deep neural network |
DRNN | Deep recurrent neural network |
DT | Decision tree |
FH | Frankfort horizontal |
GD | Group display |
IIA | Inter-incisal angle |
IMPA | Incisor mandibular plane angle |
k-NN | k-nearest neighbor |
L1 | Lower central incisor |
LOP | Lower occlusal plane |
LR | Logistic regression |
LSTM | Long short-term memory |
MLP | Multi-layer perceptron |
NA | Nasion to point-A |
NB | Naive Bayesian |
NB | Nasion to point-B |
NC | Non-cleft |
NLD | Non-linear dynamics |
NSCLP ± P | Non-syndromic cleft lip and palate with or without palate |
OB | Overbite |
OJ | Overjet |
PSD | Power spectrum density |
RF | Random forest |
SN | Sella nasion |
SNA | Sella, nasion, A-point |
SNB | Sella, nasion, B-point |
SNPs | Single nucleotide polymorphism |
SVM | Support vector machine |
U1 | Upper central Incisor |
UCL | Unilateral cleft lip |
UCLA | Unilateral cleft lip and alveolus |
UCLP | Unilateral CLP |
UID | Upper incisor display |
UOP | Upper occlusal plane, |
VGG | Visual Geometry Group |
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Nos | Keyword Strings | Results Obtained in Scopus (S) | Results Obtained in PubMed (P) | Results Obtained in Web of Science (W) | Articles Screened from Results According to Title (S + P + W) |
---|---|---|---|---|---|
1 | Craniofacial anomaly + Oral clefts * + Artificial intelligence * | 0 | 2 | 0 | 02 |
2 | Artificial intelligence * + Cleft lip and palate * + automated landmarks | 01 | 0 | 01 | 02 |
3 | Oral cleft * + Machine learning * + prediction | 02 | 0 | 03 | 05 |
4 | Neural network * + Deep learning * + Cleft lip and palate * | 03 | 0 | 06 | 09 |
5 | Machine learning * + clefts * + sagittal relationship | 01 | 0 | 0 | 01 |
6 | Machine learning * + Genetic risk + Oral clefts * | 01 | 01 | 03 | 05 |
7 | Artificial intelligence * + anatomical variations + Cleft lip and palate * | 0 | 0 | 0 | 0 |
8 | Automatic detection + hypernasal speech + Cleft lip and palate * | 05 | 0 | 06 | 11 |
9 | Cleft Lip and Palate * + Surgery + Deep learning * | 02 | 01 | 02 | 05 |
10 | Facial morphology + oral clefts * + Machine learning * | 0 | 0 | 0 | 0 |
11 | Maxillofacial defect + Machine learning * + orofacial clefts * | 0 | 0 | 0 | 0 |
12 | Speech recognition + Artificial intelligence * + Oral clefts * | 0 | 0 | 0 | 0 |
13 | Artificial intelligence * + Orthognathic surgery + Prognostics factors | 01 | 0 | 01 | 02 |
14 | Artificial intelligence * + Dental characteristics + clefts * | 0 | 0 | 02 | 02 |
Total | 16 | 04 | 24 | 44 |
Test outcome (index test) | Disease status (reference standard result) | |
True positives (a) | False positives (b) | Test positives (a + b) |
False negatives (c) | True negatives (d) | Test negatives (c + d) |
Index test positive (T+) | Index test negative (T−) |
Author Name with Year of Publication | Title of the Article | Reason for Exclusion |
---|---|---|
Orozco-Arroyave et al. [32] | Characterization methods for the detection of multiple voice disorders: Neurological, functional, and laryngeal diseases | The authors did not use any of the AI or machine learning techniques in this study. |
Dubey et al. [33] | Detection and assessment of hypernasality in repaired cleft palate speech using vocal tract and residual features | The authors used different methods for detection and assessment of hypernasality in children with CLP but no AI or machine learning methods involved in the study. |
Phan et al. [34] | Tooth agenesis and orofacial clefting: genetic brothers in arms? | This is a review paper on tooth agenesis and orofacial clefting based on genetic loci but did not mention about any AI models. |
Mathiyalagan et al. [35] | Meta-Analysis of Grainyhead-Like Dependent Transcriptional Networks: A Roadmap for Identifying Novel Conserved Genetic Pathways | The meta-analysis was done to identify the genes causing oral clefting but no AI or Machine learning techniques used in this study |
Lim et al. [36] | Determination of prognostic factors for orthognathic surgery in children with cleft lip and/or palate | Unable to download the full content of this study. |
Carvajal-Castaño and Orozco-Arroyave, [37] | Articulation Analysis in the Speech of Children with Cleft Lip and Palate | This article is a chapter from the book “Progress in Pattern Recognition Image Analysis, Computer Vision and Applications”. |
Zhang et al. [38] | Cleft Volume Estimation and Maxilla Completion Using Cascaded Deep Neural Networks | This paper is a chapter from the book “Machine Learning in Medical Imaging”. |
Tanikawa et al. [39] | Clinical applicability of automated cephalometric landmark identification: Part I—Patient-related identification errors | Unable to download the full text article. |
Author | Target Condition | Sample Size | AI Technique and Method Employed | Findings |
---|---|---|---|---|
Machado et al. [42] | Genetic risk assessment in non-syndromic CLP | 722 Brazilian subjects with NSCL ± P and 866 without NSCL ± P | RF and multi-layer NN. The genetic risk of NSCL ± P in the Brazilian population was developed by putting 72 known SNPs to RF, which was then used to identify important SNPs. Multiple regression was used to assess the interactions between the SNPs. | 13 SNPs were found to be highly predictive to detect NSCL ± P. The combination of these SNPs was able to split the controls from NSCL ± P with highest accuracy rate of 94.5%. |
Zhang et al. [43] | 504 East asians,103 Han Chinese and 279 Uyghur Chinese with CLP | SVM, LR, NB, DT, RF, k-NN, and ANN. Machine learning techniques were used to validate the diagnostic ability of 43 SNP candidates in assessing genetic risk in Chinese populations. After manual selection, a panel of 24 SNPs was assessed for risk assessment efficiency. Each time the LR-based model was trained, an SNP was removed or added in a sequential manner. | In the Han population, the LR model produced the greatest results for genetic risk assessment, whereas the SVM produced better results in the Uyghur group. The relative risk score methodology produced the greatest results in the Uyghur population. SNPs in three genes involved in folic acid and vitamin A production were found to play a critical role in the occurrence of NSCL ± P. | |
Alam et al. [44] | Sagittal jaw relationship in cleft and non-cleft individuals | 123 Saudi Arabian patients 21 BCLP, 41 UCLP, 13 UCL, 9 UCLA and 31 NC individuals | AI driven WebCeph software. The LCRs of patients were used to measure 4 different parameters such as SNA, SNB, ANB and Wits appraisal. | The comparison of sagittal development among different types of clefts with NC subjects revealed significant smaller SNA, ANB angles and Wits appraisal. However, there was no significant variation observed in SNB angle between cleft and non-cleft subjects. Also, there was no significant difference found in terms of gender and types of clefts. |
Alam and Alfawzan [19] | Dental characteristics in cleft and non- cleft individuals | 123 Saudi Arabian subjects 92 cleft and 31 non-cleft individuals | AI driven lateral cephalometric analysis was done using WebCeph software. 14 different dental characteristics such as OJ, OB U1 to FH, U1 to SN U1 to UOP, IMPA L1 to LOP, IIA, COP U1 to NA (mm), U1 to NA (degree), L1 to NB (mm), L1 to NB (degree), UID were evaluated. | Significant disparities among cleft and NC subjects were found in relation to Overjet, U1 to FH, U1 to SN, U1 to IMPA, IIA, U1 to NA (degree) and L1 to NB (degree). However, no significant differences were observed between cleft and NC in relation to OB, U1 to UOP, L1 to LOP, COP, U1 to NA (mm), L1 to NB (mm) and UID. AI based cephalometric assessment showed 95.6% accuracy. |
Wang et al. [45] | Detection of Hypernasality in cleft palate patients | 144 Chinese patients (72 with hypernasality and 72 controls) | LSTM-DRNN method which is used for automatic detection of hypernasal speech, vocal cords related feature mining, classification ability and analysis of hypernasality- sensitive vowels. | LSTM-DRNN achieved highest 91.10% accuracy in automatic hypernasal speech detection compared with shallow classifiers. The GD spectrum and PSD have shown 93.35% and 90.26% accuracy, respectively. |
Golabbakhsh et al. [46] | 15 CLP patients and 15 controls (Iranian population) | SVM. Automatic detection of hypernasality with acoustic analysis of Speech. Mel frequency, bionet wavelet transform entropy. | When combined with SVM, Mel frequency and bionet wavelet transform energy 85% of the accuracy have been achieved in identifying hypernasality. | |
Wang et al. [47] | 62 Children and 48 adults (Chinese patients) | CNN. Hypernasality detection. | A hypernasality detection accuracy of 93.34% was achieved with CNN compared with state-of-the-art literature. | |
Orozco-Arroyave et al. [48] | South American children with CLP | SVM. Automatic identification of hypernasal speech of Spanish vowels using classical and non-linear analysis | The NLD analysis provide relevant information and can be used as an alternative classical Mel frequency in automatic detection of hypernasality in Spanish vowels. The greater accuracy of 95.4% was achieved with only NLD features. | |
Orozco-Arroyave et al. [40] | Spanish subjects Cases 130 Controls 108 German subjects Cases 429 Controls 39 | A SVM was used to determine whether a voice recording is hypernasal or healthy. | It was found that the combination of NLD features and entropy measurements yield best results. The addition of information provided by the five vowels in the discriminating process results in an improvement in system performance for each vowel. | |
Mathad et al. [41] | 75 cases 251 controls (American population) | A DNN classifier was created to distinguish between nasal and non-nasal speech sounds using a healthy voice corpus. | The proposed DNN method employs forced-alignment, which could lead to incorrect segmentation and impact the hypernasality estimator’s effectiveness. | |
Li et al. [49] | Cleft lip and palate surgery | 2568 CLP cases (Chinese population) | Deep learning technique for CLP surgery. Train the model to locate surgical incisions and markers. State-of-the-art Hour glass architecture and residual learning models were used to create strong baseline dataset. | CLPNet-Light and VGG are significantly better than two CSR-based techniques. The CLPNet-Light is 2.5 times higher than CLPNet which has strong robustness and can be used to train the model to aid in surgical marker localization. |
Shafi et al. [50] | Prediction of oral cleft | 1000 Pakistani subjects (500 cases and 500 controls) | DNN. A questionnaire was designed to collect information on 36 input characteristics from mothers, half of whom had cleft babies and the other half were controls. Data was gathered and various prediction models were used. The precision of the results obtained with each were assessed. | On test data, the MLP model with three hidden layers and 28 perceptrons in each provided the highest classification accuracy rate of 92.6%. |
No | Authors | Country | Study Design | Sample Size (n) | Quality Assessment (%) | Risk of Bias Rating |
---|---|---|---|---|---|---|
1 | Machado et al. [42] | Brazil | Retrospective Case control | 1588 | 90.0 | LOW |
2 | Zhang et al. [43] | China | Retrospective Case control | 171 | 90.0 | LOW |
3 | Alam et al. [44] | Saudi Arabia | Retrospective Case control | 123 | 80.0 | LOW |
4 | Alam and Alfawzan [19] | Saudi Arabia | Retrospective Case control | 123 | 80.0 | LOW |
5 | Wang et al. [45] | China | Retrospective Case control | 144 | 60.0 | MODERATE |
6 | Golabbakhsh et al. [46] | Iran | Retrospective Case-control | 30 | 80.0 | LOW |
7 | Wang et al. [47] | China | Retrospective Case control | 110 | 80.0 | LOW |
8 | Orozco-Arroyave et al. [48] | South America | Retrospective Case control | 238 | 80.0 | LOW |
9 | Orozco-Arroyave et al. [40] | South America | Retrospective Case control | 202 | 90.0 | LOW |
10 | Mathad et al. [41] | South America | Retrospective Case control | 326 | 50.0 | HIGH |
11 | Li et al. [49] | China | Retrospective | 2568 | 50.0 | HIGH |
12 | Shafi et al. [50] | Pakistan | Prospective | 1000 | 70.0 | LOW |
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Huqh, M.Z.U.; Abdullah, J.Y.; Wong, L.S.; Jamayet, N.B.; Alam, M.K.; Rashid, Q.F.; Husein, A.; Ahmad, W.M.A.W.; Eusufzai, S.Z.; Prasadh, S.; et al. Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate—A Systematic Review. Int. J. Environ. Res. Public Health 2022, 19, 10860. https://doi.org/10.3390/ijerph191710860
Huqh MZU, Abdullah JY, Wong LS, Jamayet NB, Alam MK, Rashid QF, Husein A, Ahmad WMAW, Eusufzai SZ, Prasadh S, et al. Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate—A Systematic Review. International Journal of Environmental Research and Public Health. 2022; 19(17):10860. https://doi.org/10.3390/ijerph191710860
Chicago/Turabian StyleHuqh, Mohamed Zahoor Ul, Johari Yap Abdullah, Ling Shing Wong, Nafij Bin Jamayet, Mohammad Khursheed Alam, Qazi Farah Rashid, Adam Husein, Wan Muhamad Amir W. Ahmad, Sumaiya Zabin Eusufzai, Somasundaram Prasadh, and et al. 2022. "Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate—A Systematic Review" International Journal of Environmental Research and Public Health 19, no. 17: 10860. https://doi.org/10.3390/ijerph191710860