The Impact of Regulatory Fit on Performance in an Inattentional Blindness Paradigm

Single Versus Second Observer vs Artificial Intelligence to Increase the ADENOMA Detection Rate of Colonoscopy-A Network Analysis

BACKGROUND AND AIMS

Screening colonoscopy has significantly contributed to the reduction of the incidence of colorectal cancer (CRC) and its associated mortality, with adenoma detection rate (ADR) as the quality marker. To increase the ADR, various solutions have been proposed including the utilization of Artificial Intelligence (AI) and employing second observers during colonoscopies. In the interest of AI improving ADR independently, without a second observer, and the operational similarity between AI and second observer, this network meta-analysis aims at evaluating the effectiveness of AI, second observer, and a single observer in improving ADR.

METHODS

We searched the Medline, Embase, Cochrane, Web of Science Core Collection, Korean Citation Index, SciELO, Global Index Medicus, and Cochrane. A direct head-to-head comparator analysis and network meta-analysis were performed using the random-effects model. The odds ratio (OR) was calculated with a 95% confidence interval (CI) and p-value < 0.05 was considered statistically significant.

RESULTS

We analyzed 26 studies, involving 22,560 subjects. In the direct comparative analysis, AI demonstrated higher ADR (OR: 0.668, 95% CI 0.595-0.749, p < 0.001) than single observer. Dual observer demonstrated a higher ADR (OR: 0.771, 95% CI 0.688-0.865, p < 0.001) than single operator. In network meta-analysis, results were consistent on the network meta-analysis, maintaining consistency. No statistical difference was noted when comparing AI to second observer. (RR 1.1 (0.9-1.2, p = 0.3). Results were consistent when evaluating only RCTs. Net ranking provided higher score to AI followed by second observer followed by single observer.

CONCLUSION

Artificial Intelligence and second-observer colonoscopy showed superior success in Adenoma Detection Rate when compared to single-observer colonoscopy. Although not statistically significant, net ranking model favors the superiority of AI to the second observer.

Individual differences in inattentional blindness

People often fail to notice unexpected objects and events when they are performing an attention-demanding task, a phenomenon known as inattentional blindness. We might expect individual differences in cognitive ability or personality to predict who will and will not notice unexpected objects given that people vary in their ability to perform attention-demanding tasks. We conducted a comprehensive literature search for empirical inattentional blindness reports and identified 38 records that included individual difference measures and met our inclusion criteria. From those, we extracted individual difference effect sizes for 31 records which included a total of 74 distinct, between-groups samples with at least one codable individual difference measure. We conducted separate meta-analyses of the relationship between noticing/missing an unexpected object and scores on each of the 14 cognitive and 19 personality measures in this dataset. We also aggregated across personality measures reflecting positive/negative affectivity or openness/absorption and cognitive measures of interference, attention breadth, and memory. Collectively, these meta-analyses provided little evidence that individual differences in ability or personality predict noticing of an unexpected object. A robustness analysis that excluded samples with extremely low numbers of people who noticed or missed produced similar results. For most measures, the number of samples and the total sample sizes were small, and larger studies are needed to examine individual differences in inattentional blindness more systematically. However, the results are consistent with the idea that noticing of unexpected objects or events differs from deliberate attentional control tasks in that it is not reliably predicted by individual differences in cognitive ability.

Team Regulatory Strategies and Performance in Elite Handball

Purposes: The purposes of this research were: 1) in a preliminary study, to identify regulatory strategies used by players and coaches according to situation reward structure (Gains vs. Losses); and 2) in the main study, to analyze the effect of the interaction between situation reward structure (Gains vs. Losses) and regulatory strategy (Prevention vs. Promotion) on team performance (scoring a goal) in real-world settings. Methods and Results: In the preliminary study, 25 adult male handball players and 19 coaches playing and coaching at national level in the French championships completed a version of the Regulatory Focus Strategies Scales after reading four handball game scenarios (two gain-oriented and two loss-avoidance oriented). Two-way ANOVA with repeated measures revealed that when the reward structure was gain-oriented, participants had higher scores in promotion regulatory strategy than in prevention regulatory strategy, and conversely. For the main study, a sample of 199 game phases was selected from 84 games played in the Lidl Star League (2017-2018 season). Mixed method analysis revealed a significant team regulatory fit effect, that is an interaction effect between reward structure and regulatory focus strategy on team performance. When there was a fit, both in loss-avoidance oriented and gain-oriented reward structure, performance was higher (OR = 1.64 and OR = 0.86, respectively) than when there was a mismatch (OR = 0.59 and OR = 0.27). Conclusion: These results confirm the applicability of the Regulatory Fit Theory in a real-world setting with professional players, and extend grounded social cognition research regulatory fit literature to team level.

The application of artificial intelligence in improving colonoscopic adenoma detection rate: Where are we and where are we going

Colorectal cancer (CRC) is one of the common malignant tumors in the world. Colonoscopy is the crucial examination technique in CRC screening programs for the early detection of precursor lesions, and treatment of early colorectal cancer, which can reduce the morbidity and mortality of CRC significantly. However, pooled polyp miss rates during colonoscopic examination are as high as 22%. Artificial intelligence (AI) provides a promising way to improve the colonoscopic adenoma detection rate (ADR). It might assist endoscopists in avoiding missing polyps and offer an accurate optical diagnosis of suspected lesions. Herein, we described some of the milestone studies in using AI for colonoscopy, and the future application directions of AI in improving colonoscopic ADR.

The Role of an Artificial Intelligence Method of Improving the Diagnosis of Neoplasms by Colonoscopy

BACKGROUND

Colorectal cancer (CRC) is the third most common cancer worldwide. Colonoscopy is the gold standard examination that reduces the morbidity and mortality of CRC. Artificial intelligence (AI) could be useful in reducing the errors of the specialist and in drawing attention to the suspicious area.

METHODS

A prospective single-center randomized controlled study was conducted in an outpatient endoscopy unit with the aim of evaluating the usefulness of AI-assisted colonoscopy in PDR and ADR during the day time. It is important to understand how already available CADe systems improve the detection of polyps and adenomas in order to make a decision about their routine use in practice. In the period from October 2021 to February 2022, 400 examinations (patients) were included in the study. One hundred and ninety-four patients were examined using the ENDO-AID CADe artificial intelligence device (study group), and 206 patients were examined without the artificial intelligence (control group).

RESULTS

None of the analyzed indicators (PDR and ADR during morning and afternoon colonoscopies) showed differences between the study and control groups. There was an increase in PDR during afternoon colonoscopies, as well as ADR during morning and afternoon colonoscopies.

CONCLUSIONS

Based on our results, the use of AI systems in colonoscopies is recommended, especially in circumstances of an increase of examinations. Additional studies with larger groups of patients at night are needed to confirm the already available data.

Artificial intelligence empowers the second-observer strategy for colonoscopy: a randomized clinical trial

Background

In colonoscopy screening for colorectal cancer, human vision limitations may lead to higher miss rate of lesions; artificial intelligence (AI) assistance has been demonstrated to improve polyp detection. However, there still lacks direct evidence to demonstrate whether AI is superior to trainees or experienced nurses as a second observer to increase adenoma detection during colonoscopy. In this study, we aimed to compare the effectiveness of assistance from AI and human observer during colonoscopy.

Methods

A prospective multicenter randomized study was conducted from 2 September 2019 to 29 May 2020 at four endoscopy centers in China. Eligible patients were randomized to either computer-aided detection (CADe)-assisted group or observer-assisted group. The primary outcome was adenoma per colonoscopy (APC). Secondary outcomes included polyp per colonoscopy (PPC), adenoma detection rate (ADR), and polyp detection rate (PDR). We compared continuous variables and categorical variables by using R studio (version 3.4.4).

Results

A total of 1,261 (636 in the CADe-assisted group and 625 in the observer-assisted group) eligible patients were analysed. APC (0.42 vs 0.35, P = 0.034), PPC (1.13 vs 0.81, P < 0.001), PDR (47.5% vs 37.4%, P < 0.001), ADR (25.8% vs 24.0%, P = 0.464), the number of detected sessile polyps (683 vs 464, P < 0.001), and sessile adenomas (244 vs 182, P = 0.005) were significantly higher in the CADe-assisted group than in the observer-assisted group. False detections of the CADe system were lower than those of the human observer (122 vs 191, P < 0.001).

Conclusions

Compared with the human observer, the CADe system may improve the clinical outcome of colonoscopy and reduce disturbance to routine practice (Chictr.org.cn No.: ChiCTR1900025235).

Real-time colorectal polyp detection using a novel computer-aided detection system (CADe): a feasibility study

BACKGROUND AND AIMS

Colonoscopy aims to early detect and remove precancerous colorectal polyps, thereby preventing development of colorectal cancer (CRC). Recently, computer-aided detection (CADe) systems have been developed to assist endoscopists in polyp detection during colonoscopy. The aim of this study was to investigate feasibility and safety of a novel CADe system during real-time colonoscopy in three European tertiary referral centers.

METHODS

Ninety patients undergoing colonoscopy assisted by a real-time CADe system (DISCOVERY; Pentax Medical, Tokyo, Japan) were prospectively included. The CADe system was turned on only at withdrawal, and its output was displayed on secondary monitor. To study feasibility, inspection time, polyp detection rate (PDR), adenoma detection rate (ADR), sessile serrated lesion (SSL) detection rate (SDR), and the number of false positives were recorded. To study safety, (severe) adverse events ((S)AEs) were collected. Additionally, user friendliness was rated from 1 (worst) to 10 (best) by endoscopists.

RESULTS

Mean inspection time was 10.8 ± 4.3 min, while PDR was 55.6%, ADR 28.9%, and SDR 11.1%. The CADe system users estimated that < 20 false positives occurred in 81 colonoscopy procedures (90%). No (S)AEs related to the CADe system were observed during the 30-day follow-up period. User friendliness was rated as good, with a median score of 8/10.

CONCLUSION

Colonoscopy with this novel CADe system in a real-time setting was feasible and safe. Although PDR and SDR were high compared to previous studies with other CADe systems, future randomized controlled trials are needed to confirm these detection rates. The high SDR is of particular interest since interval CRC has been suggested to develop frequently through the serrated neoplasia pathway.

CLINICAL TRIAL REGISTRATION

The study was registered in the Dutch Trial Register (reference number: NL8788).

The Effect of Judgement Bias on Cue Utilization for Shot Prediction in Basketball Athletes

BACKGROUND

Concerning the judgments bias and cue utilization in basketball athletes, previous shot anticipation tasks were hard to examine in regards to whether the experts' judgement bias relies more on the cue of the player's body or the ball trajectory.

METHODS

Four types of body-ball cues shots were employed: IN-IN, IN-OUT, OUT-IN, and OUT-OUT. Four temporal stages (i.e., shooting, rising, high point, and falling) were divided during a shot. Forty-two participants predicted the fate of the ball after watching the shot videos.

RESULTS

The results suggested that for the shooting, rising, and high point phase, compared to the non-athletes, the experts provided superior predictions for IN-IN condition and OUT-IN condition but fewer accurate predictions for IN-OUT condition and OUT-OUT condition. Moreover, a higher bias toward predicting the shots as "in" for the athletes than the non-athletes under early temporal conditions was confirmed.

CONCLUSIONS

These findings strengthen the idea that the IN cues from both body information and ball trajectory could elicit the experts' judgement bias for made shots and then influence their response, thus rendered two distinct (e.g., impeding and facilitating) effects for the incongruent body-ball cues, respectively.

The single-monitor trial: an embedded CADe system increased adenoma detection during colonoscopy: a prospective randomized study

BACKGROUND

Computer-aided detection (CADe) of colon polyps has been demonstrated to improve colon polyp and adenoma detection during colonoscopy by indicating the location of a given polyp on a parallel monitor. The aim of this study was to investigate whether embedding the CADe system into the primary colonoscopy monitor may serve to increase polyp and adenoma detection, without increasing physician fatigue level.

METHODS

Consecutive patients presenting for colonoscopies were prospectively randomized to undergo routine colonoscopy with or without the assistance of a real-time polyp detection CADe system. Fatigue level was evaluated from score 0 to 10 by the performing endoscopists after each colonoscopy procedure. The main outcome was adenoma detection rate (ADR).

RESULTS

Out of 790 patients analyzed, 397 were randomized to routine colonoscopy (control group), and 393 to a colonoscopy with computer-aided diagnosis (CADe group). The ADRs were 20.91% and 29.01%, respectively (OR = 1.546, 95% CI 1.116-2.141, p = 0.009). The average number of adenomas per colonoscopy (APC) was 0.29 and 0.48, respectively (Change Folds = 1.64, 95% CI 1.299-2.063, p < 0.001). The improvement in polyp detection was mainly due to increased detection of non-advanced diminutive adenomas, serrated adenoma and hyperplastic polyps. The fatigue score for each procedure was 3.28 versus 3.40 for routine and CADe group, p = 0.357.

CONCLUSIONS

A real-time CADe system employed on the primary endoscopy monitor may lead to improvements in ADR and polyp detection rate without increasing fatigue level during colonoscopy. The integration of a low-latency and high-performance CADe systems may serve as an effective quality assurance tool during colonoscopy. www.chictr.org.cn number, ChiCTR1800018058.

Computer aided detection for laterally spreading tumors and sessile serrated adenomas during colonoscopy

Background

Evidence has shown that deep learning computer aided detection (CADe) system achieved high overall detection accuracy for polyp detection during colonoscopy.

Aim

The detection performance of CADe system on non-polypoid laterally spreading tumors (LSTs) and sessile serrated adenomas/polyps (SSA/Ps), with higher risk for malignancy transformation and miss rate, has not been exclusively investigated.

Methods

A previously validated deep learning CADe system for polyp detection was tested exclusively on LSTs and SSA/Ps. 1451 LST images from 184 patients were collected between July 2015 and January 2019, 82 SSA/Ps videos from 26 patients were collected between September 2018 and January 2019. The per-frame sensitivity and per-lesion sensitivity were calculated.

Results

(1) For LSTs image dataset, the system achieved an overall per-image sensitivity and per-lesion sensitivity of 94.07% (1365/1451) and 98.99% (197/199) respectively. The per-frame sensitivity for LST-G(H), LST-G(M), LST-NG(F), LST-NG(PD) was 93.97% (343/365), 98.72% (692/701), 85.71% (324/378) and 85.71% (6/7) respectively. The per-lesion sensitivity of each subgroup was 100.00% (71/71), 100.00% (64/64), 98.31% (58/59) and 80.00% (4/5). (2) For SSA/Ps video dataset, the system achieved an overall per-frame sensitivity and per-lesion sensitivity of 84.10% (15883/18885) and 100.00% (42/42), respectively.

Conclusions

This study demonstrated that a local-feature-prioritized automatic CADe system could detect LSTs and SSA/Ps with high sensitivity. The per-frame sensitivity for non-granular LSTs and small SSA/Ps should be further improved.

The effects of sport expertise and shot results on basketball players' action anticipation

The purpose of the present cross-sectional study was to clarify the effects of sport expertise and shot results on the action anticipation of basketball players. Eighty-eight male subjects participated in this study, namely, 30 collegiate basketball players, 28 recreational basketball players and 30 non-athletes. Each participant performed a shot anticipation task in which he watched the shooting phase, rising phase, high point and falling phase of a free throw and predicted the fate of the ball. The results showed that the collegiate players and recreational players demonstrated higher accuracy than the non-athletes for the falling phase but not for the other temporal conditions. Analysis of the shot results demonstrated that for made shots, the collegiate players and recreational players provided more accurate predictions than the non-athletes. These results suggested that the experienced players required a sufficient amount of information to be able to make accurate judgements and demonstrated that the experts’ judgement bias for made shots was independent of the temporal condition.

Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study

OBJECTIVE

The effect of colonoscopy on colorectal cancer mortality is limited by several factors, among them a certain miss rate, leading to limited adenoma detection rates (ADRs). We investigated the effect of an automatic polyp detection system based on deep learning on polyp detection rate and ADR.

DESIGN

In an open, non-blinded trial, consecutive patients were prospectively randomised to undergo diagnostic colonoscopy with or without assistance of a real-time automatic polyp detection system providing a simultaneous visual notice and sound alarm on polyp detection. The primary outcome was ADR.

RESULTS

Of 1058 patients included, 536 were randomised to standard colonoscopy, and 522 were randomised to colonoscopy with computer-aided diagnosis. The artificial intelligence (AI) system significantly increased ADR (29.1%vs20.3%, p<0.001) and the mean number of adenomas per patient (0.53vs0.31, p<0.001). This was due to a higher number of diminutive adenomas found (185vs102; p<0.001), while there was no statistical difference in larger adenomas (77vs58, p=0.075). In addition, the number of hyperplastic polyps was also significantly increased (114vs52, p<0.001).

CONCLUSIONS

In a low prevalent ADR population, an automatic polyp detection system during colonoscopy resulted in a significant increase in the number of diminutive adenomas detected, as well as an increase in the rate of hyperplastic polyps. The cost-benefit ratio of such effects has to be determined further.

TRIAL REGISTRATION NUMBER

ChiCTR-DDD-17012221; Results.

Reviewing Regulatory Focus Based on Four Regulatory Forms

This study employs the four regulatory forms (goal pursuit, goal maintenance, negative escape, and active avoidance) to illuminate the heterogeneousness among regulatory focus measurements and activations. The first two studies consistently found that promotion focus involves goal pursuit orientation; however, prevention focus encompasses a goal maintenance and a negative escape orientation. The regulatory forms were then applied to regulatory fit research to investigate how the matches of regulatory forms determine the effect sizes of regulatory fit. By meta-analyses, the weak effect in one third of regulatory fit studies, whose regulatory forms were mismatched or partially matched, decreased the overall fit effect and increased the heterogeneousness among regulatory fit studies. However, a strong and consistent regulatory fit effect was found in well-matched of regulatory forms. By examining and extending regulatory forms to measurement, activation, and regulatory fit studies, this paper offers further understanding of the mechanisms of regulatory focus.

The Perils of Automaticity

Classical theories of skill acquisition propose that automatization (i.e., performance requires progressively less attention as experience is acquired) is a defining characteristic of expertise in a variety of domains (e.g., Fitts & Posner, 1967 ). Automaticity is believed to enhance smooth and efficient skill execution by allowing performers to focus on strategic elements of performance rather than on the mechanical details that govern task implementation ( Williams & Ford, 2008 ). By contrast, conscious processing (i.e., paying conscious attention to one's action during motor execution) has been found to disrupt skilled movement and performance proficiency (e.g., Beilock & Carr, 2001 ). On the basis of this evidence, researchers have tended to extol the virtues of automaticity. However, few researchers have considered the wide range of empirical evidence which indicates that highly automated behaviors can, on occasion, lead to a series of errors that may prove deleterious to skilled performance. Therefore, the purpose of the current paper is to highlight the perils, rather than the virtues, of automaticity. We draw on Reason's (1990) classification scheme of everyday errors to show how an overreliance on automated procedures may lead to 3 specific performance errors (i.e., mistakes, slips, and lapses) in a variety of skill domains (e.g., sport, dance, music). We conclude by arguing that skilled performance requires the dynamic interplay of automatic processing and conscious processing in order to avoid performance errors and to meet the contextually contingent demands that characterize competitive environments in a range of skill domains.

Some See It, Some Don't: Exploring the Relation between Inattentional Blindness and Personality Factors

Human awareness is highly limited, which is vividly demonstrated by the phenomenon that unexpected objects go unnoticed when attention is focused elsewhere (inattentional blindness). Typically, some people fail to notice unexpected objects while others detect them instantaneously. Whether this pattern reflects stable individual differences is unclear to date. In particular, hardly anything is known about the influence of personality on the likelihood of inattentional blindness. To fill this empirical gap, we examined the role of multiple personality factors, namely the Big Five, BIS/BAS, absorption, achievement motivation, and schizotypy, in these failures of awareness. In a large-scale sample (N = 554), susceptibility to inattentional blindness was associated with a low level of openness to experience and marginally with a low level of achievement motivation. However, in a multiple regression analysis, only openness emerged as an independent, negative predictor. This suggests that the general tendency to be open to experience extends to the domain of perception. Our results complement earlier work on the possible link between inattentional blindness and personality by demonstrating, for the first time, that failures to consciously perceive unexpected objects reflect individual differences on a fundamental dimension of personality.

Lassen sich Leistungsunterschiede im Basketballfreiwurf durch die Regulatory Focus Theorie und die Handlungskontrolltheorie erklären?

Nach der Regulatory Focus Theorie von Higgins (1997) kann ein Ziel mit einem Promotion-Fokus (Fokus liegt auf Hoffnung und Erfüllung) oder Prevention-Fokus (Fokus liegt auf Pflicht und Sicherheit) erreicht werden. Ein Leistungsvorteil ergibt sich, wenn der chronische Fokus mit der Situation übereinstimmt („Regulatory Fit“). Die Handlungskontrolltheorie von Kuhl (1983, 2001) erklärt Zielerreichungsprozesse mittels handlungsorientierten (zügige Umsetzung der Intention) und lageorientierten Personen (kognitive Auseinandersetzung führt zur Handlungsblockade). Diese Studie überprüft, ob die Konstrukte der Regulatory Focus- und der Handlungskontrolltheorie im Zusammenhang stehen. Zudem wird überprüft, ob der Regulatory Fit-Effekt sowie die Handlungsorientierung ähnliche Effekte bezüglich der Trefferleistung beim Freiwurf während einer Doppelaufgabe erklären können. Basketballspieler der Ober-/Landesliga stehen zur Verfügung. Die Ergebnisse zeigen einen Zusammenhang bezüglich der Skalen zur Erfassung der Handlungsorientierung und des regulatorischen Fokus. Leistungsstarke Basketballspieler profitieren signifikant von einer Prevention-Instruktion. Der Zusammenhang zwischen den Theorien sowie deren Vorhersage auf die sportliche Leistung wird diskutiert.

Nurse observation during colonoscopy increases polyp detection: a randomized prospective study

OBJECTIVES

To determine whether a second observer during colonoscopy increases adenoma detection.

METHODS

Consecutive patients undergoing screening colonoscopy were prospectively randomized to routine colonoscopy or physician and nurse observation during withdrawal.

RESULTS

Of 502 patients, 249 were randomized to routine colonoscopy, and 253 to physician plus nurse observation during withdrawal. A total of 592 polyps were detected, 40 identified by the endoscopy nurse only. With nurse observation, 1.32 polyps and 0.82 adenomas were found per colonoscopy, vs. 1.03 polyps and 0.64 adenomas in the routine group, demonstrating a 1.29-fold and a 1.28-fold increase in the average number of polyps and of adenomas detected, respectively. The overall adenoma detection rate (ADR) was 44.1%, with trends toward increased ADR and all-polyp detection rate with nurse observation.

CONCLUSIONS

Nurse observation during colonoscopy resulted in an increase in the number of polyps and adenomas found per colonoscopy, along with a trend toward improved overall ADR and all-polyp detection rate.