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Volume 10, Issue 1
  • ISSN: 2212-697X
  • E-ISSN: 2212-6988

Abstract

In recent years, there has been a notable increase in the global incidence of oral cancer, leading to significant morbidity and mortality, especially when diagnosed at advanced stages. The integration of technology holds great promise for early detection and diagnosis, facilitating improved patient management for clinicians. The emergence of artificial intelligence (AI) presents a potential breakthrough in oral cancer screening, as it can meticulously analyze vast datasets from various imaging modalities, offering valuable support in the field of oncology. This review focuses on a spectrum of AI techniques utilized for early detection and diagnosis of oral cancer. Additionally, AI techniques may be employed for the effective treatment of oral cancer. Using the abundance of information acquired, this article provides an in-depth overview and discussion of AI's value and benefits in oral cancer screening, early detection, disease prediction, and therapy, among other areas. Furthermore, it identifies present limits and forecasts the hopeful future of AI in oral cancer research.

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2024-08-29
2025-01-19

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References

  1. FitzmauriceC. DickerD. PainA. HamavidH. Moradi-LakehM. MacIntyreM.F. AllenC. HansenG. WoodbrookR. WolfeC. HamadehR.R. MooreA. WerdeckerA. GessnerB.D. Te AoB. McMahonB. KarimkhaniC. YuC. CookeG.S. SchwebelD.C. CarpenterD.O. PereiraD.M. NashD. KaziD.S. De LeoD. PlassD. UkwajaK.N. ThurstonG.D. YunJin K.; Simard, E.P.; Mills, E.; Park, E.K.; Catalá-López, F.; deVeber, G.; Gotay, C.; Khan, G.; Hosgood, H.D., III; Santos, I.S.; Leasher, J.L.; Singh, J.; Leigh, J.; Jonas, J.B.; Sanabria, J.; Beardsley, J.; Jacobsen, K.H.; Takahashi, K.; Franklin, R.C.; Ronfani, L.; Montico, M.; Naldi, L.; Tonelli, M.; Geleijnse, J.; Petzold, M.; Shrime, M.G.; Younis, M.; Yonemoto, N.; Breitborde, N.; Yip, P.; Pourmalek, F.; Lotufo, P.A.; Esteghamati, A.; Hankey, G.J.; Ali, R.; Lunevicius, R.; Malekzadeh, R.; Dellavalle, R.; Weintraub, R.; Lucas, R.; Hay, R.; Rojas-Rueda, D.; Westerman, R.; Sepanlou, S.G.; Nolte, S.; Patten, S.; Weichenthal, S.; Abera, S.F.; Fereshtehnejad, S.M.; Shiue, I.; Driscoll, T.; Vasankari, T.; Alsharif, U.; Rahimi-Movaghar, V.; Vlassov, V.V.; Marcenes, W.S.; Mekonnen, W.; Melaku, Y.A.; Yano, Y.; Artaman, A.; Campos, I.; MacLachlan, J.; Mueller, U.; Kim, D.; Trillini, M.; Eshrati, B.; Williams, H.C.; Shibuya, K.; Dandona, R.; Murthy, K.; Cowie, B.; Amare, A.T.; Antonio, C.A.; Castañeda-Orjuela, C.; van Gool, C.H.; Violante, F.; Oh, I.H.; Deribe, K.; Soreide, K.; Knibbs, L.; Kereselidze, M.; Green, M.; Cardenas, R.; Roy, N.; Tillmann, T.; Li, Y.; Krueger, H.; Monasta, L.; Dey, S.; Sheikhbahaei, S.; Hafezi-Nejad, N.; Kumar, G.A.; Sreeramareddy, C.T.; Dandona, L.; Wang, H.; Vollset, S.E.; Mokdad, A.; Salomon, J.A.; Lozano, R.; Vos, T.; Forouzanfar, M.; Lopez, A.; Murray, C.; Naghavi, M. The global burden of cancer 2013.JAMA Oncol.20151450552710.1001/jamaoncol.2015.073526181261
     [Google Scholar]
  2. SiegelR.L. MillerK.D. WagleN.S. JemalA. Cancer statistics, 2023.CA Cancer J. Clin.2023731174810.3322/caac.2176336633525
     [Google Scholar]
  3. SoniV. Mucoadhesive film for local delivery to oral cancer: Formulation development, box–behnken experimental design, and in vitro characterization.Asian J. Pharm.2022164
     [Google Scholar]
  4. MorikawaT. KozakaiA. KosugiA. BesshoH. ShibaharaT. Image processing analysis of oral cancer, oral potentially malignant disorders, and other oral diseases using optical instruments.Int. J. Oral Maxillofac. Surg.202049451552110.1016/j.ijom.2019.08.01631500953
     [Google Scholar]
  5. VobugariN. RajaV. SethiU. GandhiK. RajaK. SuraniS.R. Advancements in oncology with artificial intelligence—a review article.Cancers (Basel)2022145134910.3390/cancers1405134935267657
     [Google Scholar]
  6. WarnakulasuriyaS. KujanO. Aguirre-UrizarJ.M. BaganJ.V. González-MolesM.Á. KerrA.R. LodiG. MelloF.W. MonteiroL. OgdenG.R. SloanP. JohnsonN.W. Oral potentially malignant disorders: A consensus report from an international seminar on nomenclature and classification, convened by the WHO Collaborating Centre for Oral Cancer.Oral Dis.20212781862188010.1111/odi.1370433128420
     [Google Scholar]
  7. TanriverG. Soluk TekkesinM. ErgenO. Automated detection and classification of oral lesions using deep learning to detect oral potentially malignant disorders.Cancers (Basel)20211311276610.3390/cancers1311276634199471
     [Google Scholar]
  8. García-PolaM. Pons-FusterE. Suárez-FernándezC. Seoane-RomeroJ. Romero-MéndezA. López-JornetP. Role of artificial intelligence in the early diagnosis of oral cancer. A scoping review.Cancers (Basel)20211318460010.3390/cancers1318460034572831
     [Google Scholar]
  9. Al-RawiN. SultanA. RajaiB. ShuaeebH. AlnajjarM. AlketbiM. The effectiveness of artificial intelligence in detection of oral cancer.Int. Dent. J.2022722436447
     [Google Scholar]
  10. A deep learning based pipeline for efficient oral cancer screening on whole slide images. Lu, J.; Sladoje, N.; Runow Stark, C.; Darai Ramqvist, E.; Hirsch, J-M. Lindblad, J., Eds. International Conference on Image Analysis and Recognition202010.1007/978‑3‑030‑50516‑5_22
     [Google Scholar]
  11. De FauwJ. LedsamJ.R. Romera-ParedesB. NikolovS. TomasevN. BlackwellS. AskhamH. GlorotX. O’DonoghueB. VisentinD. van den DriesscheG. LakshminarayananB. MeyerC. MackinderF. BoutonS. AyoubK. ChopraR. KingD. KarthikesalingamA. HughesC.O. RaineR. HughesJ. SimD.A. EganC. TufailA. MontgomeryH. HassabisD. ReesG. BackT. KhawP.T. SuleymanM. CornebiseJ. KeaneP.A. RonnebergerO. Clinically applicable deep learning for diagnosis and referral in retinal disease.Nat. Med.20182491342135010.1038/s41591‑018‑0107‑630104768
     [Google Scholar]
  12. KhanyileR. MarimaR. MbejeM. MutambirwaS. MontwediD. DlaminiZ. AI Tools Offering Cancer Clinical Applications for Risk Predictor, Early Detection, Diagnosis, and Accurate Prognosis: Perspectives in Personalised Care.Artificial Intelligence and Precision Oncology: Bridging Cancer Research and Clinical Decision Support.Springer2023293312
     [Google Scholar]
  13. Pereira-PradoV. Martins-SilveiraF. SiccoE. HochmannJ. Isiordia-EspinozaM. GonzálezR. PandiarD. Bologna-MolinaR. Artificial Intelligence for Image Analysis in Oral Squamous Cell Carcinoma: A Review.Diagnostics (Basel)20231314241610.3390/diagnostics1314241637510160
     [Google Scholar]
  14. KumarY. MahajanM. Intelligent behavior of fog computing with IOT for healthcare system.International Journal of Scientific & Technology Research.201987674679
     [Google Scholar]
  15. RaghupathiW. RaghupathiV. Big data analytics in healthcare: promise and potential.Health Inf. Sci. Syst.201421310.1186/2047‑2501‑2‑325825667
     [Google Scholar]
  16. KaulS. KumarY. Artificial intelligence-based learning techniques for diabetes prediction: challenges and systematic review.SN Computer Science20201632210.1007/s42979‑020‑00337‑2
     [Google Scholar]
  17. BansalK. BatlaR. KumarY. ShafiJ. Artificial intelligence techniques in health informatics for oral cancer detection. Connected e-Health. Studies in Computational IntelligenceSpringer: Cham20221021255279
     [Google Scholar]
  18. GorokhovatskyiO. PeredriiO. GorokhovatskyiV. VlasenkoN. Explanation of CNN image classifiers with hiding parts.Explainable Deep Learning AI - Methods and Challenges.Elsevier2023125146
     [Google Scholar]
  19. VincentL. JayasinghJ.R. Comparison of psoriasis disease detection and classification through various image processing techniques- A review.6th International Conference on Devices, Circuits and Systems (ICDCS)2022
     [Google Scholar]
  20. SidhuH.S. BenignoS. GaneshanB. DikaiosN. JohnstonE.W. AllenC. KirkhamA. GrovesA.M. AhmedH.U. EmbertonM. TaylorS.A. HalliganS. PunwaniS. Textural analysis of multiparametric MRI detects transition zone prostate cancer.Eur. Radiol.20172762348235810.1007/s00330‑016‑4579‑927620864
     [Google Scholar]
  21. AlabiR.O. AlmangushA. ElmusratiM. MäkitieA.A. Deep machine learning for oral cancer: from precise diagnosis to precision medicine.Frontiers in Oral Health2022279424810.3389/froh.2021.79424835088057
     [Google Scholar]
  22. WarinK. LimprasertW. SuebnukarnS. JinapornthamS. JantanaP. VicharueangS. AI-based analysis of oral lesions using novel deep convolutional neural networks for early detection of oral cancer.PLoS One2022178e027350810.1371/journal.pone.027350836001628
     [Google Scholar]
  23. AwaisM. GhayvatH. Krishnan PandarathodiyilA. Nabillah GhaniW.M. RamanathanA. PandyaS. WalterN. SaadM.N. ZainR.B. FayeI. Healthcare professional in the loop (HPIL): classification of standard and oral cancer-causing anomalous regions of oral cavity using textural analysis technique in autofluorescence imaging.Sensors (Basel)20202020578010.3390/s2020578033053886
     [Google Scholar]
  24. DeyS. SinghA.K. SinghA.K. RawatK. BanerjeeJ. AgnihotriV. UpadhayaD. Critical pathways of oral squamous cell carcinoma: molecular biomarker and therapeutic intervention.Med. Oncol.20223933010.1007/s12032‑021‑01633‑435059897
     [Google Scholar]
  25. RiccardiG. BellizziM.G. FatuzzoI. ZoccaliF. CavalcantiL. GrecoA. VincentiisM. RalliM. FioreM. PetrellaC. MinniA. BarbatoC. Salivary biomarkers in oral squamous cell carcinoma: A proteomic overview.Proteomes20221043710.3390/proteomes1004003736412636
     [Google Scholar]
  26. TuttyM.A. CarlasareL.E. LloydS. SinskyC.A. The complex case of EHRs: examining the factors impacting the EHR user experience.J. Am. Med. Inform. Assoc.201926767367710.1093/jamia/ocz02130938754
     [Google Scholar]
  27. ArztM. DeschampsJ. SchmiedC. PietzschT. SchmidtD. TomancakP. HaaseR. JugF. LABKIT: labeling and segmentation toolkit for big image data.Front. Comput. Sci.2022477772810.3389/fcomp.2022.777728
     [Google Scholar]
  28. DoolinJ.W. HaakenstadE.K. NevilleB.A. LipsitzS.R. ZhangS. ClevelandJ.L.F. HiruyS. HassettM.J. RevetteA. SchragD. BaschE. McClearyN.J. Feasibility of Weekly Electronic Health Record–Embedded Patient-Reported Outcomes for Patients Starting Oral Cancer–Directed Therapy.JCO Clin. Cancer Inform.202377e230004310.1200/CCI.23.0004337788407
     [Google Scholar]
  29. BodleyT. KwanJ.L. MatelskiJ. DarraghP.J. CramP. Test result management practices of Canadian internal medicine physicians and trainees.J. Gen. Intern. Med.201934111812410.1007/s11606‑018‑4656‑730298242
     [Google Scholar]
  30. GroenhofT.K.J. RittersmaZ.H. BotsM.L. BrandjesM. JacobsJ.J.L. GrobbeeD.E. van SolingeW.W. VisserenF.L.J. HaitjemaS. AsselbergsF.W. A computerised decision support system for cardiovascular risk management ‘live’ in the electronic health record environment: development, validation and implementation—the Utrecht Cardiovascular Cohort Initiative.Neth. Heart J.201927943544210.1007/s12471‑019‑01308‑w31372838
     [Google Scholar]
  31. ZhuX. TuS.P. SewellD. YaoN.A. MishraV. DowA. BanasC. Measuring electronic communication networks in virtual care teams using electronic health records access-log data.Int. J. Med. Inform.2019128465210.1016/j.ijmedinf.2019.05.01231160011
     [Google Scholar]
  32. LinW.C. ChenJ.S. ChiangM.F. HribarM.R. Applications of artificial intelligence to electronic health record data in ophthalmology.Transl. Vis. Sci. Technol.2020921310.1167/tvst.9.2.1332704419
     [Google Scholar]
  33. LalA. PinevichY. GajicO. HerasevichV. PickeringB. Artificial intelligence and computer simulation models in critical illness.World J. Crit. Care Med.202092131910.5492/wjccm.v9.i2.1332577412
     [Google Scholar]
  34. SatpathyS. ManglaM. SharmaN. DeshmukhH. MohantyS. Predicting mortality rate and associated risks in COVID-19 patients.Spat. Inf. Res.202129455464
     [Google Scholar]
  35. UthoffR.D. SongB. SunnyS. PatrickS. SureshA. KolurT. KeerthiG. SpiresO. AnbaraniA. Wilder-SmithP. KuriakoseM.A. BirurP. LiangR. Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities.PLoS One20181312e020749310.1371/journal.pone.020749330517120
     [Google Scholar]
  36. SongB. SunnyS. UthoffR.D. PatrickS. SureshA. KolurT. KeerthiG. AnbaraniA. Wilder-SmithP. KuriakoseM.A. BirurP. RodriguezJ.J. LiangR. Automatic classification of dual-modalilty, smartphone-based oral dysplasia and malignancy images using deep learning.Biomed. Opt. Express20189115318532910.1364/BOE.9.00531830460130
     [Google Scholar]
  37. MahmoodH. ShabanM. IndaveB.I. Santos-SilvaA.R. RajpootN. KhurramS.A. Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review.Oral Oncol.202011010488510.1016/j.oraloncology.2020.10488532674040
     [Google Scholar]
  38. Chiesa-EstombaC.M. GrañaM. MedelaA. Sistiaga-SuarezJ.A. LechienJ.R. Calvo-HenriquezC. Mayo-YanezM. VairaL.A. GrammaticaA. CammarotoG. AyadT. FaganJ.J. Machine learning algorithms as a computer-assisted decision tool for oral cancer prognosis and management decisions: a systematic review.ORL J. Otorhinolaryngol. Relat. Spec.202284427828810.1159/00052067235021182
     [Google Scholar]
  39. AdeoyeJ. Koohi-MoghadamM. LoA.W.I. TsangR.K.Y. ChowV.L.Y. ZhengL.W. ChoiS.W. ThomsonP. SuY.X. Deep learning predicts the malignant-transformation-free survival of oral potentially malignant disorders.Cancers (Basel)20211323605410.3390/cancers1323605434885164
     [Google Scholar]
  40. ChuC.S. LeeN.P. AdeoyeJ. ThomsonP. ChoiS.W. Machine learning and treatment outcome prediction for oral cancer.J. Oral Pathol. Med.2020491097798510.1111/jop.1308932740951
     [Google Scholar]
  41. JamesB.L. SunnyS.P. HeidariA.E. RamanjinappaR.D. LamT. TranA.V. KankanalaS. SilS. TiwariV. PatrickS. PillaiV. ShettyV. HedneN. ShahD. ShahN. ChenZ. KandasarmaU. RaghavanS.A. GurudathS. NagarajP.B. Wilder-SmithP. SureshA. KuriakoseM.A. Validation of a point-of-care optical coherence tomography device with machine learning algorithm for detection of oral potentially malignant and malignant lesions.Cancers (Basel)20211314358310.3390/cancers1314358334298796
     [Google Scholar]
  42. JubairF. Al-karadshehO. MalamosD. Al MahdiS. SaadY. HassonaY. A novel lightweight deep convolutional neural network for early detection of oral cancer.Oral Dis.20222841123113010.1111/odi.1382533636041
     [Google Scholar]
  43. MarzoukR. AlabdulkreemE. DhahbiS. NourM.K. DuhayyimM.A. OthmanM. Deep Transfer Learning Driven Oral Cancer Detection and Classification Model.Comput. Mater. Continua202273210.32604/cmc.2022.029326
     [Google Scholar]
  44. NguyenP-T-H. SakamotoK. IkedaT. Deep-learning application for identifying histological features of epithelial dysplasia of tongue.J. Oral Maxillofac. Surg. Med. Pathol.202234451452210.1016/j.ajoms.2021.12.008
     [Google Scholar]
  45. RahmanT.Y. MahantaL.B. ChoudhuryH. DasA.K. SarmaJ.D. Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques.Cancer Rep.202036e129310.1002/cnr2.129333026718
     [Google Scholar]
  46. SharmaD. KudvaV. PatilV. KudvaA. BhatR.S. A convolutional neural network based deep learning algorithm for identification of oral precancerous and cancerous lesion and differentiation from normal mucosa: a retrospective study.Engineered Science20221827828710.30919/es8d663
     [Google Scholar]
  47. WarinK. LimprasertW. SuebnukarnS. JinapornthamS. JantanaP. Automatic classification and detection of oral cancer in photographic images using deep learning algorithms.J. Oral Pathol. Med.202150991191810.1111/jop.1322734358372
     [Google Scholar]
  48. WarinK. LimprasertW. SuebnukarnS. JinapornthamS. JantanaP. Performance of deep convolutional neural network for classification and detection of oral potentially malignant disorders in photographic images.Int. J. Oral Maxillofac. Surg.202251569970410.1016/j.ijom.2021.09.00134548194
     [Google Scholar]
  49. KimY. LeeK.J. SunwooL. ChoiD. NamC.M. ChoJ. KimJ. BaeY.J. YooR.E. ChoiB.S. JungC. KimJ.H. Deep Learning in Diagnosis of Maxillary Sinusitis Using Conventional Radiography.Invest. Radiol.201954171510.1097/RLI.000000000000050330067607
     [Google Scholar]
  50. KhanagarS.B. NaikS. Al KheraifA.A. VishwanathaiahS. MaganurP.C. AlhazmiY. MushtaqS. SarodeS.C. SarodeG.S. ZanzaA. TestarelliL. PatilS. Application and performance of artificial intelligence technology in oral cancer diagnosis and prediction of prognosis: a systematic review.Diagnostics (Basel)2021116100410.3390/diagnostics1106100434072804
     [Google Scholar]
  51. HegdeS. AjilaV. ZhuW. ZengC. Artificial intelligence in early diagnosis and prevention of oral cancer.Asia Pac. J. Oncol. Nurs.202291210013310.1016/j.apjon.2022.10013336389623
     [Google Scholar]
  52. HuangS. YangJ. FongS. ZhaoQ. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges.Cancer Lett.2020471617110.1016/j.canlet.2019.12.00731830558
     [Google Scholar]
  53. JeyarajP.R. Samuel NadarE.R. Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm.J. Cancer Res. Clin. Oncol.2019145482983710.1007/s00432‑018‑02834‑730603908
     [Google Scholar]
  54. RudinC. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nat. Mach. Intell.20191520621510.1038/s42256‑019‑0048‑x35603010
     [Google Scholar]
  55. DirectiveC. 93/42/EEC of 14 June 1993 Concerning.Med. Devices1993
     [Google Scholar]
  56. PesapaneF. VolontéC. CodariM. SardanelliF. Artificial intelligence as a medical device in radiology: Ethical and regulatory issues in Europe and the United States.Insights Imaging20189574575310.1007/s13244‑018‑0645‑y30112675
     [Google Scholar]
  57. Arterys Receives First FDA Clearance for Broad Oncology Imaging Suite with Deep Learning.2018Available from: https://www.prnewswire.com/news-releases/arterys-receives-first-fda-clearance-for-broad-oncology-imaging-suite-with-deep-learning-300599275.html
  58. BeraK. SchalperK.A. RimmD.L. VelchetiV. MadabhushiA. Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology.Nat. Rev. Clin. Oncol.2019161170371510.1038/s41571‑019‑0252‑y31399699
     [Google Scholar]
  59. FDA grants breakthrough designation to Paige AI. 2019Available from: https://www.businesswire.com/news/home/20190307005205/en/FDA-Grants-Breakthrough-Designation-to-Paige.AI
  60. Garcia-RojoM. De MenaD. Muriel-CuetoP. Atienza-CuevasL. Dominguez-GomezM. BuenoG. New European Union Regulations Related to Whole Slide Image Scanners and Image Analysis Software.J. Pathol. Inform.2019102
     [Google Scholar]
  61. BuenoG. Fernández-CarroblesM.M. DenizO. García-RojoM. New trends of emerging technologies in digital pathology.Pathobiology2016832-3616910.1159/00044348227100343
     [Google Scholar]
  62. ShafiS. A.V.Parwani Artificial intelligence in diagnostic pathology.Diagnostic pathology2023181109
     [Google Scholar]
  63. FDA issues warning letter to genomics lab for illegally marketing genetic test that claims to predict patients’ responses to specific medications.2019Available from: https://www.fda.gov/news-events/press-announcements/fda-issues-warning-letter-genomics-lab-illegally-marketing-genetic-test-claims-predict-patients
  64. PaxtonA. Laboratory-developed tests CAP suggests added oversight of homebrews.CAP Today2009
     [Google Scholar]
  65. MadabhushiA. LeeG. Image analysis and machine learning in digital pathology: Challenges and opportunities.Med. Image Anal.20163317017510.1016/j.media.2016.06.03727423409
     [Google Scholar]
  66. JanowczykA. MadabhushiA. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases.J. Pathol. Inform.2016712910.4103/2153‑3539.18690227563488
     [Google Scholar]
  67. Cruz-RoaA. GilmoreH. BasavanhallyA. FeldmanM. GanesanS. ShihN.N.C. TomaszewskiJ. GonzálezF.A. MadabhushiA. Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent.Sci. Rep.2017714645010.1038/srep4645028418027
     [Google Scholar]
  68. DoyleS. FeldmanM. TomaszewskiJ. MadabhushiA. A boosted Bayesian multiresolution classifier for prostate cancer detection from digitized needle biopsies.IEEE Trans. Biomed. Eng.20125951205121810.1109/TBME.2010.205354020570758
     [Google Scholar]
  69. ChingT. HimmelsteinD.S. Beaulieu-JonesB.K. KalininA.A. DoB.T. WayG.P. FerreroE. AgapowP.M. ZietzM. HoffmanM.M. XieW. RosenG.L. LengerichB.J. IsraeliJ. LanchantinJ. WoloszynekS. CarpenterA.E. ShrikumarA. XuJ. CoferE.M. LavenderC.A. TuragaS.C. AlexandariA.M. LuZ. HarrisD.J. DeCaprioD. QiY. KundajeA. PengY. WileyL.K. SeglerM.H.S. BocaS.M. SwamidassS.J. HuangA. GitterA. GreeneC.S. Opportunities and obstacles for deep learning in biology and medicine.J. R. Soc. Interface2018151412017038710.1098/rsif.2017.038729618526
     [Google Scholar]
  70. MobadersanyP. YousefiS. AmgadM. GutmanD.A. Barnholtz-SloanJ.S. Velázquez VegaJ.E. BratD.J. CooperL.A.D. Predicting cancer outcomes from histology and genomics using convolutional networks.Proc. Natl. Acad. Sci. USA201811513E2970E297910.1073/pnas.171713911529531073
     [Google Scholar]
  71. WangX. JanowczykA. ZhouY. ThawaniR. FuP. SchalperK. VelchetiV. MadabhushiA. Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images.Sci. Rep.2017711354310.1038/s41598‑017‑13773‑729051570
     [Google Scholar]
  72. CoudrayN. OcampoP.S. SakellaropoulosT. NarulaN. SnuderlM. FenyöD. MoreiraA.L. RazavianN. TsirigosA. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning.Nat. Med.201824101559156710.1038/s41591‑018‑0177‑530224757
     [Google Scholar]
  73. KothariS. PhanJ.H. MoffittR.A. StokesT.H. HassbergerS.E. ChaudryQ. YoungA.N. WangM.D. Automatic batch-invariant color segmentation of histological cancer images.Proc. IEEE Int. Symp. Biomed. Imaging2011201165766010.1109/ISBI.2011.587249227532016
     [Google Scholar]
  74. VahadaneA. PengT. SethiA. AlbarqouniS. WangL. BaustM. SteigerK. SchlitterA.M. EspositoI. NavabN. Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images.IEEE Trans. Med. Imaging20163581962197110.1109/TMI.2016.252966527164577
     [Google Scholar]
  75. TizhooshH.R. PantanowitzL. Artificial intelligence and digital pathology: challenges and opportunities.J. Pathol. Inform.2018913810.4103/jpi.jpi_53_1830607305
     [Google Scholar]
  76. MontaltoM.C. An industry perspective: An update on the adoption of whole slide imaging.J. Pathol. Inform.2016718
     [Google Scholar]
  77. DasN. HussainE. MahantaL.B. Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network.Neural Netw.2020128476010.1016/j.neunet.2020.05.00332416467
     [Google Scholar]
  78. AlabiR.O. BelloI.O. YoussefO. ElmusratiM. MäkitieA.A. AlmangushA. Utilizing deep machine learning for prognostication of oral squamous cell carcinoma-a systematic review.Frontiers in Oral Health2021268686310.3389/froh.2021.68686335048032
     [Google Scholar]
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Innovative Progress: Artificial Intelligence in the Realm of Oral Cancer
Clinical Cancer Drugs 10, E2212697X315512 (2024); https://doi.org/10.2174/012212697X315512240821045542
/content/journals/ccand/10.2174/012212697X315512240821045542
/content/journals/ccand/10.2174/012212697X315512240821045542
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  • Article Type:     Review Article
Keyword(s): Artificial intelligence; cardiovascular disease; deep learning; image analysis; machine learning; oral cancer

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