Table 3.

Studies That Applied ML for Management of Other Ocular Cancers (i.e., Non-Rb, and Noniris/UM)

Author	Year	Type(s) of Cancer	Data Source	No. of Data Points	Data Modality	ML Algorithm(s) Used	Key Takeaways	Conflicts of Interest	Funding Sources
Diagnosis
Habibalahi et al.	2019	Ocular surface squamous neoplasia	Human	18	Auto fluorescence multispectral images of biopsy specimens	K-nearest neighbor, SVM	Multispectral autofluorescence imaging can be used in combination with ML to detect the boundary of OSSN	None	Personal Eyes Pty Ltd; ARC Centre of Excellence for Nanoscale Biophotonics; Australian research council; iMQRES scholarship
Yoo et al.	2021	Conjunctival melanoma	Images from Google and smartphone images from synthetic eye models	398	Eye images	5 different CNN architectures	The algorithm achieved an accuracy of 87.5% in 4 class classification (conjunctival melanoma, conjunctival nevus, and melanosis, pterygium, and normal conjunctiva) and 97.2% for binary classification (conjunctival melanoma or not)	Ik Hee Ryu and Jin Kuk Kim are executives of VISUWORKS, Inc., which is a Korean artificial intelligence startup providing medical ML solutions	Aerospace Medical Center of Korea Air Force, South Korea
Hou et al.	2021	Adnexal lymphoma	Human	56	MRI	SVM with linear kernel	SVM with contrast-enhanced MRI was significantly better than the radiology resident in differentiating between ocular adnexal lymphoma and idiopathic orbital inflammation	None	National Natural Science Foundation of China; China Postdoctoral Science Foundation; Shaanxi Key R&D Plan; Xi'an Science and Technology Plan Project; Scientific Research Foundation of Xi'an Fourth Hospital
Xie et al.	2022	Ocular adnexal lymphoma	Human	89	Patient and tumor characteristics	CNN	The model trained on a combination of clinical and imaging data had an AUC of 0.95 in differentiating between ocular adnexal lymphoma and idiopathic orbital inflammation	None	National Natural Science Foundation of China; Shaanxi Key R&D Plan; Shaanxi International Science and Technology Cooperation Program; China Postdoctoral Science Foundation; Xi'an Science and Technology Plan
Luo et al.	2022	Eyelid basal cell and sebaceous carcinoma	Human	296	H&E–stained sections	CNN	CNN achieved an accuracy of 0.983 in diagnosing eyelid basal cell carcinoma and sebaceous carcinoma, compared with accuracies of 0.644, 0.729, and 0.831 for pathologists	XG is a technical consultant of Jinan Guoke Medical Engineering and Technology Development Co	National Natural Science Foundation of China; Science and Technology Commission of Shanghai; Innovative research team of high-level local universities in Shanghai; Key Research and Development Program of Shandong Province; Key Research and Development Program of Jiangsu Province; Jiangsu Province Engineering Research Center of Diagnosis and Treatment of Children's Malignant Tumour; Shandong Province Natural Science Foundation
Hui et al.	2022	Eyelid tumors	Human	345	Eye images	CNN	Eight CNNs were trained and achieved a maximum AUC of 0.889. The deep-learning systems performed comparably with senior ophthalmologists	None	National Natural Science Foundation of China; The Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospitals Authority
Prognosis
Taktak et al.	2004	Intraocular melanoma	Human	2331	Tumor characteristics	ANN	RMS error was 3.7 years for the ANN and 4.3 years for the clinical expert for 15 years survival	Not listed	Not listed
Treatment
Tan et al.	2017	Periocular basal cell carcinoma	Human	156	Patient and tumor characteristics	Naive Bayes and decision trees	A 3-variable decision tree was able to predict the operative complexity	None	None

ANN, artificial neural network; H&E, hematoxylin and eosin.

Most studies focused on diagnosis, though the literature is sparse in this field.