Leaving colorectal polyps in place can be achieved with high accuracy using blue light imaging (BLI)

White light computer-aided optical diagnosis of diminutive colorectal polyps in routine clinical practice

Background and study aims Artificial Intelligence (AI) systems could make the optical diagnosis (OD) of diminutive colorectal polyps (DCPs) more reliable and objective. This study was aimed at prospectively evaluating feasibility and diagnostic performance of AI-standalone and AI-assisted OD of DCPs in a real-life setting by using a white light-based system (GI Genius, Medtronic Co, Minneapolis, Minnesota, United States). Patients and methods Consecutive colonoscopy outpatients with at least one DCP were evaluated by 11 endoscopists (5 experts and 6 non-experts in OD). DCPs were classified in real time by AI (AI-standalone OD) and by the endoscopist with the assistance of AI (AI-assisted OD), with histopathology as the reference standard. Results Of the 480 DCPs, AI provided the outcome "adenoma" or "non-adenoma" in 81.4% (95% confidence interval [CI]: 77.5-84.6). Sensitivity, specificity, positive and negative predictive value, and accuracy of AI-standalone OD were 97.0% (95% CI 94.0-98.6), 38.1% (95% CI 28.9-48.1), 80.1% (95% CI 75.2-84.2), 83.3% (95% CI 69.2-92.0), and 80.5% (95% CI 68.7-82.8%), respectively. Compared with AI-standalone, the specificity of AI-assisted OD was significantly higher (58.9%, 95% CI 49.7-67.5) and a trend toward an increase was observed for other diagnostic performance measures. Overall accuracy and negative predictive value of AI-assisted OD for experts and non-experts were 85.8% (95% CI 80.0-90.4) vs. 80.1% (95% CI 73.6-85.6) and 89.1% (95% CI 75.6-95.9) vs. 80.0% (95% CI 63.9-90.4), respectively. Conclusions Standalone AI is able to provide an OD of adenoma/non-adenoma in more than 80% of DCPs, with a high sensitivity but low specificity. The human-machine interaction improved diagnostic performance, especially when experts were involved.

Application of artificial intelligence in endoscopic gastrointestinal tumors

With an increasing number of patients with gastrointestinal cancer, effective and accurate early diagnostic clinical tools are required provide better health care for patients with gastrointestinal cancer. Recent studies have shown that artificial intelligence (AI) plays an important role in the diagnosis and treatment of patients with gastrointestinal tumors, which not only improves the efficiency of early tumor screening, but also significantly improves the survival rate of patients after treatment. With the aid of efficient learning and judgment abilities of AI, endoscopists can improve the accuracy of diagnosis and treatment through endoscopy and avoid incorrect descriptions or judgments of gastrointestinal lesions. The present article provides an overview of the application status of various artificial intelligence in gastric and colorectal cancers in recent years, and the direction of future research and clinical practice is clarified from a clinical perspective to provide a comprehensive theoretical basis for AI as a promising diagnostic and therapeutic tool for gastrointestinal cancer.

Blue-light imaging or narrow-band imaging for proximal colonic lesions: a prospective randomized tandem colonoscopy study

BACKGROUND AND AIMS

Blue-light imaging (BLI) is a new image-enhanced endoscopy with a wavelength filter similar to narrow-band imaging (NBI). We compared the 2 with white-light imaging (WLI) on proximal colonic lesion detection and miss rates.

METHODS

In this 3-arm prospective randomized study with tandem examination of the proximal colon, we enrolled patients aged ≥40 years. Eligible patients were randomized in 1:1:1 ratio to receive BLI, NBI, or WLI during the first withdrawal from the proximal colon. The second withdrawal was performed using WLI in all patients. Primary outcomes were proximal polyp (pPDRs) and adenoma (pADRs) detection rates. Secondary outcomes were miss rates of proximal lesions found on tandem examination.

RESULTS

Of 901 patients included (mean age, 64.7 years; 52.9% men), 48.1% underwent colonoscopy for screening or surveillance. The corresponding pPDRs of the BLI, NBI, and WLI groups were 45.8%, 41.6, and 36.6%, whereas the corresponding pADRs were 36.6%, 33.8%, and 28.3%. There was a significant difference in pPDR and pADR between BLI and WLI groups (difference, 9.2% [95% confidence interval {CI}, 3.3-16.9] and 8.3% [95% CI, 2.7-15.9]) and between NBI and WLI groups (difference, 5.0% [95% CI, 1.4-12.9] and 5.6% [95% CI, 2.1-13.3]). Proximal adenoma miss rates were significantly lower with BLI (19.4%) than with WLI (27.4%; difference, -8.0%; 95% CI, -15.8 to -.1) but not between NBI (27.2%) and WLI.

CONCLUSIONS

Both BLI and NBI were superior to WLI on detecting proximal colonic lesions, but only BLI had lower proximal adenoma miss rates than WLI. (Clinical trial registration number: NCT03696992.).

Comparison of LED and LASER Colonoscopy About Linked Color Imaging and Blue Laser/Light Imaging of Colorectal Tumors in a Multinational Study

INTRODUCTION

In light-emitting diode (LED) and LASER colonoscopy, linked color imaging (LCI) and blue light/laser imaging (BLI) are used for lesion detection and characterization worldwide. We analyzed the difference of LCI and BLI images of colorectal lesions between LED and LASER in a multinational study.

METHODS

We prospectively observed lesions with white light imaging (WLI), LCI, and BLI using both LED and LASER colonoscopies from January 2020 to August 2021. Images were graded by 27 endoscopists from nine countries using the polyp visibility score: 4 (excellent), 3 (good), 2 (fair), and 1 (poor) and the comparison score (LED better/similar/LASER better) for WLI/LCI/BLI images of each lesion.

RESULTS

Finally, 32 lesions (polyp size: 20.0 ± 15.2 mm) including 9 serrated lesions, 13 adenomas, and 10 T1 cancers were evaluated. The polyp visibility scores of LCI/WLI for international and Japan-expert endoscopists were 3.17 ± 0.73/3.17 ± 0.79 (p = 0.92) and 3.34 ± 0.78/2.84 ± 1.22 (p < 0.01) for LED and 3.30 ± 0.71/3.12 ± 0.77 (p < 0.01) and 3.31 ± 0.82/2.78 ± 1.23 (p < 0.01) for LASER. Regarding the comparison of lesion visibility about between LED and LASER colonoscopy in international endoscopists, a significant difference was achieved not for WLI, but for LCI. The rates of LED better/similar/LASER better for brightness under WLI were 54.5%/31.6%/13.9% (International) and 75.0%/21.9%/3.1% (Japan expert). Those under LCI were 39.2%/35.4%/25.3% (International) and 31.3%/53.1%/15.6% (Japan expert). There were no significant differences in the diagnostic accuracy and the comparison score of BLI images between LED and LASER.

CONCLUSIONS

The differences of lesion visibility for WLI/LCI/BLI between LED and LASER in international endoscopists could be compared to those in Japanese endoscopists.

Real-World Validation of a Computer-Aided Diagnosis System for Prediction of Polyp Histology in Colonoscopy: A Prospective Multicenter Study

INTRODUCTION

Computer-aided diagnosis (CADx) of polyp histology could support endoscopists in clinical decision-making. However, this has not been validated in a real-world setting.

METHODS

We performed a prospective, multicenter study comparing CADx and endoscopist predictions of polyp histology in real-time colonoscopy. Optical diagnosis based on visual inspection of polyps was made by experienced endoscopists. After this, the automated output from the CADx support tool was recorded. All imaged polyps were resected for histological assessment. Primary outcome was difference in diagnostic performance between CADx and endoscopist prediction of polyp histology. Subgroup analysis was performed for polyp size, bowel preparation, difficulty of location of the polyps, and endoscopist experience.

RESULTS

A total of 661 eligible polyps were resected in 320 patients aged ≥40 years between March 2021 and July 2022. CADx had an overall accuracy of 71.6% (95% confidence interval [CI] 68.0-75.0), compared with 75.2% (95% CI 71.7-78.4) for endoscopists ( P = 0.023). The sensitivity of CADx for neoplastic polyps was 61.8% (95% CI 56.9-66.5), compared with 70.3% (95% CI 65.7-74.7) for endoscopists ( P < 0.001). The interobserver agreement between CADx and endoscopist predictions of polyp histology was moderate (83.1% agreement, κ 0.661). When there was concordance between CADx and endoscopist predictions, the accuracy increased to 78.1%.

DISCUSSION

The overall diagnostic accuracy and sensitivity for neoplastic polyps was higher in experienced endoscopists compared with CADx predictions, with moderate interobserver agreement. Concordance in predictions increased this diagnostic accuracy. Further research is required to improve the performance of CADx and to establish its role in clinical practice.

Images of laser and light-emitting diode colonoscopy for comparing large colorectal lesion visibility with linked color imaging and white-light imaging

OBJECTIVES

In light-emitting diode (LED) and laser colonoscopy, linked color imaging (LCI) superiority to white-light imaging (WLI) for polyp detection is shown separately. We analyzed the noninferiority of LCI between LED and laser colonoscopy and that of WLI (LECOL study).

METHODS

We prospectively collected nonpolypoid lesions with WLI and LCI using LED and laser colonoscopy from January 2021 to August 2021. All images were evaluated randomly by 12 endoscopists (six nonexperts and six experts in three institutions) using the polyp visibility score: 4, excellent; 3, good; 2, fair; and 1, poor. The comparison score (LED better/similar/laser better) for redness and brightness was evaluated for WLI and LCI pictures of each lesion.

RESULTS

Finally, 63 nonpolypoid lesions were evaluated, and the mean polyp size was 24.5 ± 13.4 mm. Histopathology revealed 13 serrated lesions and 50 adenomatous/cancerous lesions. The mean polyp visibility scores of LCI pictures were significantly higher than those of WLI in the LED (3.35 ± 0.85 vs. 3.08 ± 0.91, P < 0.001) and the laser (3.40 ± 1.71 vs. 3.05 ± 0.97, P < 0.001) group, and the noninferiority of LCI pictures between LED and laser was significant (P < 0.001). The comparison scores revealed that the evaluation of redness and brightness (LED better/similar/laser better) were 26.8%/40.1%/33.1% and 43.5%/43.5%/13.0% for LCI pictures (P < 0.001) and 20.6%/44.3%/35.1% and 60.3%/31.7%/8.0% for WLI pictures (P < 0.001), respectively.

CONCLUSION

The noninferiority of polyp visibility with WLI and LCI in LED and laser colonoscopy was shown. WLI and LCI of LED tended to be brighter and less reddish than those of laser.

Performance of a novel computer-aided diagnosis system in the characterization of colorectal polyps, and its role in meeting Preservation and Incorporation of Valuable Endoscopic Innovations standards set by the American Society of Gastrointestinal Endoscopy

Background and aims

There has been an increasing role of artificial intelligence (AI) in the characterization of colorectal polyps. Recently, a novel AI algorithm for the characterization of polyps was developed by NEC Corporation (Japan). The aim of our study is to perform an external validation of this algorithm.

Methods

The study was a video-based evaluation of the computer-aided diagnosis (CADx) system. Patients undergoing colonoscopy were recruited to record videos of colonic polyps. The frozen polyp images extracted from these videos were used for real-time histological prediction by the endoscopists and by the CADx system, and the results were compared.

Results

A total of 115 polyp images were extracted from 66 patients. Sensitivity, negative predictive value and accuracy for diminutive polyps on white light imaging (WLI) and image-enhanced endoscopy (IEE) when assessed by CADx was 90.9% [95% confidence interval (CI) 77.3-100] and 95.8% [95% CI 87.5-100], 80% [95% CI 44.4-97.5] and 90.9% [95% CI 58.7-99.8], 84.8% [95% CI 72.7-97] and 84.6% [95%CI 71.8-94.9], respectively, compared to 48.1% [95%CI 37.7-59.1] and 72% [95% CI 62.5-81], 37.5% [95% CI 28.8-46.8] and 55% [95% CI 44.7-65.0], 53.7% [95% CI 44.2-63.2] and 66.7% [95% CI 59.7-73.3] when assessed by endoscopists. Concordance between histology and CADx-based post-polypectomy surveillance intervals was 93.02% on WLI and 96% on IEE.

Conclusion

AI-based optical diagnosis is promising and has the potential to be better than the performance of general endoscopists. We believe that AI can help make real-time optical diagnoses of polyps meeting the Preservation and Incorporation of Valuable endoscopic Innovations standards set by the American Society of Gastrointestinal Endoscopy.

The Efficacy of Tumor Characterization for Colorectal Lesions with Blue Light Imaging of a Compact Light-Emitting Diode Endoscopic System Compared to a Laser Endoscopic System: A Pilot Study

Background: A compact and cost-effective light source-processor combined 3-color light-emitting diode (LED) endoscopic system (ELUXEO-Lite: EP-6000, Fujifilm Co., Tokyo) with a magnified colonoscope (EC-6600ZP, Fujifilm Co.) has been released. Aims: In this study, we analyzed the efficacy of this system for colorectal tumor characterization with magnified blue light imaging (BLI-LED) and image's subjective and objective evaluations, compared to a magnified blue laser imaging (BLI-LASER) using a standard LASER endoscopic system. Methods: We retrospectively reviewed 37 lesions observed with both BLI-LED and BLI-LASER systems from 2019 using the Japanese narrow band imaging classification. Two representative magnified images, one BLI-LED and one BLI-LASER, of the same area of a lesion were evaluated for diagnostic accuracy and visualization quality by three experts and three non-experts. Their color difference values (CDVs) and brightness values (BVs) were also calculated as objective indicators. Results: Among 37 lesions, mean tumor size was 18.9 ± 13.1 mm, and 21 lesions were nonpolypoid. Histopathology revealed 14 sessile serrated lesions, 7 adenomas, 12 high-grade dysplasias and T1a cancers, and 4 T1b cancers. The diagnostic accuracy rates of BLI-LED/BLI-LASER of experts and non-experts were 90.1% and 87.4% (p = 0.52) and 89.2% and 89.2% (p = 0.99). The percentages of instances where BLI-LED images were better, the two imaging types were equivalent, or BLI-LASER images were better were 16%/83%/1% for experts and 19%/58%/23% for non-experts (p < 0.001). CDVs and BVs between BLI-LED and BLI-LASER were not significantly different (CDVs: p = 0.653, BVs: p = 0.518). Conclusions: BLI-LED using the compact system was noninferior to BLI-LASER for colorectal tumor characterization and image quality.

Artificial intelligence-assisted colonoscopy: a narrative review of current data and clinical applications

Colonoscopy is the reference standard procedure for the prevention and diagnosis of colorectal cancer, which is a leading cause of cancer-related deaths in Singapore. Artificial intelligence systems are automated, objective and reproducible. Artificial intelligence-assisted colonoscopy has recently been introduced into clinical practice as a clinical decision support tool. This review article provides a summary of the current published data and discusses ongoing research and current clinical applications of artificial intelligence-assisted colonoscopy.

Real-time diagnostic accuracy of blue light imaging, linked color imaging and white-light endoscopy for colorectal polyp characterization

Background and study aims  Fujifilm has developed a novel ELUXEO 7000 endoscope system that employs light-emitting diodes (LEDs) at four different wavelengths as light sources that enable blue light imaging (BLI), linked color imaging (LCI), and high-definition white-light endoscopy (HD-WLE). The aim of this study was to address the diagnostic accuracy of real-time polyp characterization using BLI, LCI and HD-WLE (ELUXEO 7000 endoscopy system). Patients methods  This is a prespecified post-hoc analysis of a prospective study in which 22 experienced endoscopists (> 2,000 colonoscopies) from eight international centers participated. Using a combination of BLI, LCI, and HD-WLE, lesions were endoscopically characterized including a high- or low-confidence statement. Per protocol, digital images were created from all three imaging modalities. Histopathology was the reference standard. Endoscopists were familiar with polyp characterization, but did not take dedicated training for purposes of this study. Results  Overall, 341 lesions were detected in 332 patients. Of the lesions, 269 histologically confirmed polyps with an optical diagnosis were included for analysis (165 adenomas, 27 sessile serrated lesions, and 77 hyperplastic polyps). Overall, polyp characterization was performed with high confidence in 82.9 %. The overall accuracy for polyp characterization was 75.1 % (95 % confidence interval [CI] 69.5-80.1 %), compared with an accuracy of 78.0 % (95 % CI 72.0-83.2 %) for high confidence assignments. The accuracy for endoscopic characterization for diminutive polyps was 74.7 % (95 %CI 68.4-80.3 %), compared with an accuracy of 78.2 % (95 % CI 71.4-84.0 %) for high-confidence assignments. Conclusions  The diagnostic accuracy of BLI, LCI, and HD-WLE by experienced endoscopist for real-time polyp characterization seems limited (NCT03344289).

Automatic image and text-based description for colorectal polyps using BASIC classification

Colorectal polyps (CRP) are precursor lesions of colorectal cancer (CRC). Correct identification of CRPs during in-vivo colonoscopy is supported by the endoscopist's expertise and medical classification models. A recent developed classification model is the Blue light imaging Adenoma Serrated International Classification (BASIC) which describes the differences between non-neoplastic and neoplastic lesions acquired with blue light imaging (BLI). Computer-aided detection (CADe) and diagnosis (CADx) systems are efficient at visually assisting with medical decisions but fall short at translating decisions into relevant clinical information. The communication between machine and medical expert is of crucial importance to improve diagnosis of CRP during in-vivo procedures. In this work, the combination of a polyp image classification model and a language model is proposed to develop a CADx system that automatically generates text comparable to the human language employed by endoscopists. The developed system generates equivalent sentences as the human-reference and describes CRP images acquired with white light (WL), blue light imaging (BLI) and linked color imaging (LCI). An image feature encoder and a BERT module are employed to build the AI model and an external test set is used to evaluate the results and compute the linguistic metrics. The experimental results show the construction of complete sentences with an established metric scores of BLEU-1 = 0.67, ROUGE-L = 0.83 and METEOR = 0.50. The developed CADx system for automatic CRP image captioning facilitates future advances towards automatic reporting and may help reduce time-consuming histology assessment.

Colonoscopy and artificial intelligence: Bridging the gap or a gap needing to be bridged?

Research in artificial intelligence (AI) in gastroenterology has increased over the last decade. Colonoscopy represents the most widely published field with regards to its use in gastroenterology. Most studies to date center on polyp detection and characterization, as well as real-time evaluation of adequacy of mucosal exposure for inspection. This review article discusses how advances in AI has bridged certain gaps in colonoscopy. In addition, the gaps formed with the development of AI that currently prevent its routine use in colonoscopy will be explored.

Clinically Available Optical Imaging Technologies in Endoscopic Lesion Detection: Current Status and Future Perspective

Endoscopic optical imaging technologies for the detection and evaluation of dysplasia and early cancer have made great strides in recent decades. With the capacity of in vivo early detection of subtle lesions, they allow modern endoscopists to provide accurate and effective optical diagnosis in real time. This review mainly analyzes the current status of clinically available endoscopic optical imaging techniques, with emphasis on the latest updates of existing techniques. We summarize current coverage of these technologies in major hospital departments such as gastroenterology, urology, gynecology, otolaryngology, pneumology, and laparoscopic surgery. In order to promote a broader understanding, we further cover the underlying principles of these technologies and analyze their performance. Moreover, we provide a brief overview of future perspectives in related technologies, such as computer-assisted diagnosis (CAD) algorithms dealing with exploring endoscopic video data. We believe all these efforts will benefit the healthcare of the community, help endoscopists improve the accuracy of diagnosis, and relieve patients' suffering.

Clinical Validation of BASIC Classification for the Resect and Discard Strategy for Diminutive Colorectal Polyps

BACKGROUND & AIMS

Blue-light imaging (BLI) is a chromoendoscopy technique that uses direct (not filtered) emission of blue light with short wavelength (410 nm) to increase visibility of microvascular pattern and superficial mucosa. A BLI-based classification system for colorectal polyps (also called BLI Adenomas Serrated International Classification, BASIC) has been created and was validated using still images or short videos. We aimed to validate BASIC in a clinical practice setting, using thresholds recommended by the American Society for Gastrointestinal Endoscopy for the resect and discard strategy as the reference standard.

METHODS

We studied 333 patients (mean age, 62.7±8.1 y; 176 men) who underwent screening colonoscopy from January through July 2019. Six endoscopists trained in BASIC participated in the study. All detected diminutive polyps were characterized by real-time BLI and categorized as adenoma or non-adenoma according to BASIC. All polyps were removed and evaluated by histopathology. The BLI-directed surveillance intervals (based on high-confidence characterization of polyps 5 mm or smaller and pathology feature for others) were compared with histology-directed surveillance intervals, according to United States Multi-society Task Force and European Society of Gastrointestinal Endoscopy recommendations. We calculated negative-predictive values of optical real-time analysis of diminutive rectosigmoid adenomas.

RESULTS

When we applied BASIC, 748 polyps smaller than 5 mm were categorized with 89% accuracy (95% CI, 85.9%-90.6%). BLI-directed surveillance was correct for 90% of patients according to the United States Multi-society task force criteria (95% CI, 86%-93%) and for 96% of patients according to European Society of Gastrointestinal Endoscopy criteria (95% CI, 93%-97%). The negative-predictive value for 302 polyps smaller than 5 mm, located in the rectosigmoid colon and evaluated with high confidence, based on histologic features of adenomatous polyps, was 91% (95% CI, 85%-95%).

CONCLUSIONS

Our analysis of data from 333 patients undergoing screen colonoscopies supports the validity of BASIC discriminating diminutive colorectal polyps with histologic features of adenomas from non-adenomas. This allows for the implementation of the resect and discard strategy based on BLI in clinical practice. ClinicalTrials.gov no: NCT03746171.

A CNN CADx System for Multimodal Classification of Colorectal Polyps Combining WL, BLI, and LCI Modalities

Colorectal polyps are critical indicators of colorectal cancer (CRC). Blue Laser Imaging and Linked Color Imaging are two modalities that allow improved visualization of the colon. In conjunction with the Blue Laser Imaging (BLI) Adenoma Serrated International Classification (BASIC) classification, endoscopists are capable of distinguishing benign and pre-malignant polyps. Despite these advancements, this classification still prevails a high misclassification rate for pre-malignant colorectal polyps. This work proposes a computer aided diagnosis (CADx) system that exploits the additional information contained in two novel imaging modalities, enabling more informative decision-making during colonoscopy. We train and benchmark six commonly used CNN architectures and compare the results with 19 endoscopists that employed the standard clinical classification model (BASIC). The proposed CADx system for classifying colorectal polyps achieves an area under the curve (AUC) of 0.97. Furthermore, we incorporate visual explanatory information together with a probability score, jointly computed from White Light, Blue Laser Imaging, and Linked Color Imaging. Our CADx system for automatic polyp malignancy classification facilitates future advances towards patient safety and may reduce time-consuming and costly histology assessment.

The efficacy of tumor characterization and tumor detectability of linked color imaging and blue laser imaging with an LED endoscope compared to a LASER endoscope

OBJECTIVES

An endoscope with a light-emitting diode (LED) light source which has a 2-mm close-distance observation function without magnification, has been marketed, enabling linked color imaging (LCI) and blue laser imaging (BLI) for tumor detection and characterization. We analyzed the efficacy of a LED endoscope compared to a LASER endoscope.

METHODS

We retrospectively reviewed 272 lesions observed using the LED endoscopic system (Fujifilm Co., Tokyo, Japan) from May 2018 to September 2019. The Japanese NBI Classification was used for tumor characterization. We analyzed the diagnostic accuracy and confidence level. Sixty-one lesions observed with both the LED and magnified LASER endoscopes were also analyzed to compare the diagnostic accuracy. Regarding the tumor detectability, we calculated color difference values (CDVs) and brightness values (BVs) of white-light imaging, BLI, and LCI modes between the two endoscopes for each tumor.

RESULTS

The mean polyp size was 9.2 ± 11.3 mm. Histology showed 71 sessile serrated lesions, 193 adenoma and high-grade dysplasias, and 8 T1 cancers. The diagnostic accuracy of tumors ≥ 10 and < 10 mm was 72.0% and 92.9% (p < 0.001), respectively and the high confidence rate was 93.8%. The diagnostic accuracy of LED (77.0%) was a little higher than that of LASER without magnification (65.6%, p = 0.16) but was not inferior to that of LASER with magnification (82.0%, p = 0.50). The respective CDVs of LED and LASER endoscopes were 20.6 ± 11.2 and 21.6 ± 11.2 for LCI (p = 0.30), and the respective BVs were 210.0 ± 24.2 and 175.9 ± 21.1 (p < 0.001).

CONCLUSIONS

A LED endoscope with close-distance observation improved tumor detection and characterization due to high brightness.

Optical Diagnosis of Colorectal Polyps: Recent Developments

PURPOSE OF REVIEW

Optical diagnosis of diminutive colorectal polyps has been recently proposed as an alternative to histopathologic diagnosis. Recent developments in imaging techniques, new classification systems, and the use of artificial intelligence have allowed for increased viability of optical diagnosis. This review provides an up-to-date overview of optical diagnosis recommendations, classifications, outcomes, and recent developments.

RECENT FINDINGS

There are currently seven major classification systems and three major society recommendations for quality benchmarks for optical diagnosis of diminutive polyps. The NICE classification has been extensively studied and meets quality benchmarks for most imaging techniques but does not allow for the diagnosis of sessile serrated polyps (SSPs). The SIMPLE classification has met quality benchmarks for NBI and i-Scan and allows for the diagnosis of SSPs. Other classification systems need to be further studied to validate effectiveness. Computer-assisted diagnosis of colorectal polyps is a very promising recent development with first studies showing that society-recommended quality benchmarks for real-time colonoscopies on patients are being met. Limitations include a non-negligible percentage of failure to diagnose, low specificity, and low number of real-time diagnostic studies. More research needs to be performed to further understand the value of artificial intelligence for optical polyp diagnosis. Optical diagnosis of diminutive colorectal polyps is currently a viable strategy for experienced endoscopists using validated classifications and imaging-enhanced endoscopy. Artificial intelligence-based diagnosis could make optical diagnosis widely applicable but is currently in its early developmental stage.